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marketinsightt · 1 month ago

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Autonomous Agents Market Size, Share, Trends, Growth Opportunities, Key Drivers and Competitive Outlook

Autonomous Agents Market Market Segmentation, By Deployment Type (Cloud, On-Premises), Organization Size (Small & Medium-Sized Enterprises (SMEs), Large Enterprises), Industry Vertical (BFSI, IT &Telecom, Healthcare, Manufacturing, Transportation & Mobility), - Industry Trends and Forecast to 2032

The Global Autonomous Agents Market size was valued at USD 3.9 Billion in 2024 and is expected to reach USD 51.2 Billion by 2032, at a CAGR of 44.5% during the forecast period.

This Autonomous Agents Market Market report is designed with the scrupulous market analysis carried out by a team of industry experts, dynamic analysts, skilful forecasters and well-informed researchers. And not to mention, the report is amazingly characterized by using several charts, graphs and tables depending on the extent of data and information involved. What is more, influencing factors such as market drivers, market restraints and competitive analysis is studied with the SWOT analysis which is the most established tool when it comes to generate market research report. Businesses can achieve complete knowhow of general market conditions and tendencies with the information and data covered in this Autonomous Agents Market Market report.

Autonomous Agents Market Market report comprises of all the crucial market parameters and hence it can be used for your business. Furthermore, complete company profiles covered in this report also explains what recent developments, product launches, joint ventures, mergers and acquisitions are taking place by the numerous key players and brands in the market. The Autonomous Agents Market Market report is provided with the transparent research studies which have taken place by a team work of experts in their own domain. This market report also endows with company profiles and contact information of the key market players in the manufacturer’s section.

Discover the latest trends, growth opportunities, and strategic insights in our comprehensive Autonomous Agents Market Market report. Download Full Report: https://www.databridgemarketresearch.com/reports/global-autonomous-agents-market

Autonomous Agents Market Market Overview

**Segments**

- **Type**: The global autonomous agents market can be segmented based on type into physical autonomous agents and virtual autonomous agents. Physical autonomous agents are robots, drones, and autonomous vehicles that can perform tasks in the physical world, while virtual autonomous agents are AI-powered software programs that operate in the digital space.

- **Application**: In terms of application, the market can be categorized into healthcare, retail, automotive, aerospace, defense, and others. Autonomous agents are increasingly being used in healthcare for patient monitoring and diagnostics, in retail for inventory management and customer service, in automotive for self-driving cars, and in aerospace and defense for surveillance and security purposes.

- **Technology**: The technological segmentation of the market includes machine learning, natural language processing, computer vision, and context-aware computing. These technologies enable autonomous agents to understand and interact with their environment, making them more intelligent and adaptive in various scenarios.

**Market Players**

- **IBM Corporation**: IBM offers a range of autonomous agent solutions for businesses, including AI-powered chatbots for customer service and virtual assistants for enterprise applications.

- **Amazon Web Services**: AWS provides cloud-based autonomous agent services that leverage machine learning and natural language processing to enable businesses to build intelligent applications.

- **Google**: Google is a key player in the autonomous agents market with its AI-powered virtual assistants like Google Assistant and applications of autonomous agents in self-driving cars through its subsidiary Waymo.

- **Microsoft Corporation**: Microsoft offers autonomous agent technologies through its Azure cloud platform, enabling developers to create intelligent agents for a wide range of applications.

- **Oracle Corporation**: Oracle provides autonomous agent solutions for businesses in various industries, including AI-driven chatbots for customer service and AI-powered analytics for decision-making.

The global autonomous agents market is poised for significant growth as businesses across industries adopt these intelligent agents to automate tasks, improve efficiency, and enhance customer experiences. With advancements in AI technologies such as machine learning and natural language processing, autonomous agents are becoming more sophisticated and capable of handling complex tasks. The market players mentioned above are at the forefront of this industry, driving innovation and creating new opportunities for businesses to leverage autonomous agents in their operations.

https://www.databridgemarketresearch.com/reports/global-autonomous-agents-marketThe global autonomous agents market is experiencing a transformative shift driven by advancements in artificial intelligence and automation technologies. Autonomous agents, both physical and virtual, are revolutionizing industries such as healthcare, retail, automotive, aerospace, and defense by offering innovative solutions for efficiency, productivity, and customer engagement. The market is witnessing a surge in demand for autonomous agents due to their ability to perform tasks independently, learn from interactions, and adapt to changing environments.

One of the key trends shaping the autonomous agents market is the growing integration of machine learning, natural language processing, computer vision, and context-aware computing technologies. These advancements are enabling autonomous agents to enhance their cognitive capabilities, enabling them to understand complex data, interact more effectively with users, and make intelligent decisions in real-time. As a result, businesses are increasingly turning to autonomous agents to streamline operations, deliver personalized services, and improve overall performance.

Moreover, the adoption of autonomous agents is driven by the need for automation and digital transformation in various industries. In healthcare, autonomous agents are being used for remote patient monitoring, diagnosis, and personalized treatment recommendations. In retail, autonomous agents are transforming the customer experience through virtual shopping assistants, personalized recommendations, and inventory management. In the automotive sector, self-driving cars powered by autonomous agents are redefining transportation and safety standards.

Looking ahead, the market players, including IBM Corporation, Amazon Web Services, Google, Microsoft Corporation, and Oracle Corporation, are actively investing in research and development to enhance their autonomous agent offerings and stay ahead of the competition. These companies are leveraging their expertise in AI, cloud computing, and data analytics to develop innovative solutions that address the evolving needs of businesses across industries.

Furthermore, regulatory initiatives and standards around data privacy, security, and ethical AI are influencing the development and adoption of autonomous agents. As businesses navigate these complex regulatory landscapes, market players are focusing on building transparent and accountable autonomous agent systems that comply with industry regulations and standards.

In conclusion, the global autonomous agents market is positioned for robust growth driven by technological innovation, industry adoption, and market player strategies. As businesses continue to embrace automation and AI-driven solutions, autonomous agents will play a pivotal role in shaping the future of work, customer interactions, and industry operations. The market landscape is evolving rapidly, creating new opportunities for businesses to leverage autonomous agents for competitive advantage, efficiency gains, and enhanced customer experiences.The global autonomous agents market is witnessing a significant transformation fueled by the rapid advancements in artificial intelligence and automation technologies. Businesses are increasingly turning to autonomous agents, both physical and virtual, to drive innovation, enhance efficiency, and improve customer engagement across various industries such as healthcare, retail, automotive, aerospace, and defense. This shift towards autonomous agents is driven by their ability to operate independently, learn from interactions, and adapt to changing environments, resulting in streamlined operations and personalized services.

A key trend shaping the autonomous agents market is the integration of cutting-edge technologies such as machine learning, natural language processing, computer vision, and context-aware computing. These technologies are empowering autonomous agents to enhance their cognitive capabilities, enabling them to understand complex data, interact effectively with users, and make intelligent decisions in real-time. Businesses are leveraging these advanced capabilities to deliver personalized services, optimize operations, and drive overall performance improvements.

Furthermore, the adoption of autonomous agents is gaining momentum due to the increasing emphasis on automation and digital transformation across industries. In healthcare, autonomous agents are revolutionizing patient care through remote monitoring, diagnosis, and personalized treatment recommendations. In retail, these agents are reshaping the customer experience by providing virtual shopping assistants, personalized recommendations, and efficient inventory management solutions. The automotive sector is also experiencing a significant shift with the introduction of self-driving cars powered by autonomous agents, redefining transportation and safety standards.

Looking ahead, market players such as IBM Corporation, Amazon Web Services, Google, Microsoft Corporation, and Oracle Corporation are actively investing in research and development to enhance their autonomous agent offerings and maintain a competitive edge. These companies are leveraging their expertise in AI, cloud computing, and data analytics to develop innovative solutions that meet the evolving needs of businesses across different sectors. Additionally, regulatory initiatives around data privacy, security, and ethical AI are shaping the development and adoption of autonomous agents, driving the focus on transparent and compliant autonomous agent systems.

In conclusion, the global autonomous agents market is poised for substantial growth driven by technological innovation, industry adoption, and strategic initiatives by key market players. As businesses continue to embrace automation and AI-driven solutions, autonomous agents will continue to play a crucial role in reshaping workflows, customer interactions, and industry operations. The evolving market landscape presents new opportunities for businesses to leverage autonomous agents for competitive advantages, operational efficiencies, and enhanced customer experiences, positioning them for success in the increasingly digital and automated future.

The Autonomous Agents Market Market is highly fragmented, featuring intense competition among both global and regional players striving for market share. To explore how global trends are shaping the future of the top 10 companies in the keyword market.

Learn More Now: https://www.databridgemarketresearch.com/reports/global-autonomous-agents-market/companies

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DBMR Nucleus is a dynamic, AI-powered business intelligence platform designed to revolutionize the way organizations access and interpret market data. Developed by Data Bridge Market Research, Nucleus integrates cutting-edge analytics with intuitive dashboards to deliver real-time insights across industries. From tracking market trends and competitive landscapes to uncovering growth opportunities, the platform enables strategic decision-making backed by data-driven evidence. Whether you're a startup or an enterprise, DBMR Nucleus equips you with the tools to stay ahead of the curve and fuel long-term success.

Key Coverage in the Autonomous Agents Market Market Report:

Detailed analysis of Global Autonomous Agents Market Marketby a thorough assessment of the technology, product type, application, and other key segments of the report

Qualitative and quantitative analysis of the market along with CAGR calculation for the forecast period

Investigative study of the market dynamics including drivers, opportunities, restraints, and limitations that can influence the market growth

Comprehensive analysis of the regions of the Autonomous Agents Market Marketand their futuristic growth outlook

Competitive landscape benchmarking with key coverage of company profiles, product portfolio, and business expansion strategies

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allaboutfinancess · 2 months ago

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Stellar Innovations CEO Anish Thomas on Disrupting Outsourcing with Tech-First Excellence

Stellar Innovations, founded in 2016 in Bangalore, has rapidly grown from a small, self-funded team into a global technology powerhouse with 1,600 employees across three continents. In this exclusive conversation, Stellar Innovations Pvt Ltd CEO Anish Thomas shares insights on the company's incredible journey—from humble beginnings to becoming a leader in tech-enabled outsourcing solutions. Discover how Stellar Innovations leverages deep technology, AI, and trust-driven culture to stay ahead in high-stakes sectors like mortgage tech, cybersecurity, and genomics.

How Did Stellar Innovations Begin, And What’s Been The Turning Point In Your Journey Since 2016?

Our goal from day one was to transform the outsourcing model. Rather than relying on traditional labor arbitrage, we focused on placing technology at the heart of every process. Stellar Innovations Bangalore launched with just 16 team members and no external investors—purely driven by purpose and vision. Nearly a decade later, we’ve scaled to 1,600 employees globally, transitioning from general outsourcing to tech-first solutions in sectors such as mortgage servicing and cybersecurity. Our expansion into the Philippines is not just about growth—it’s proof that our model is replicable on a global scale.

With A Growing Global Footprint, How Do You Keep Teams Connected And Aligned?

Our formula is built on two pillars: trust and ownership. Whether it's our offices in Dallas or Dubai, we function like tightly bonded teams. What makes Stellar Innovations unique is our leadership pipeline—90% of our executives began their careers here as interns. Our 97% retention rate speaks volumes. When a junior analyst in Bangalore has the authority to innovate processes that impact U.S. mortgage operations, you’re no longer managing employees—you’re empowering leaders.

What Differentiates Your Approach In Complex Domains Like Tax Servicing And Cybersecurity?

We don’t view industries as verticals—we treat them like tech challenges waiting to be solved. During the 30% mortgage market decline in the U.S., most firms scaled back. Stellar Innovations, however, doubled client acquisitions and grew revenue by 54%. We did it by applying cutting-edge tech like AI-based risk models and blockchain-backed audit systems. Our philosophy is simple: if something’s always been done a certain way, it’s probably due for disruption.

With Ai And Cybersecurity Evolving Fast, How Does Stellar Stay Ahead?

While many talk about emerging tech, we focus on practical implementation. Our team at Stellar Innovations Bangalore recently partnered with mortgage experts to deploy generative AI, resulting in a 40% drop in underwriting errors. In cybersecurity, our approach goes beyond patching vulnerabilities—we simulate real-world attacks monthly using ethical hackers to test our systems. Innovation here isn’t theoretical; it’s operational.

What’s Next On The Horizon For Stellar Innovations?

We’re stepping into telecom and genomics—two high-potential sectors. Our planned acquisition of a telecom company in Germany will help us apply predictive AI to 5G network management. At the same time, our genomics venture, Mediomix, is pioneering quantum computing solutions to cut DNA sequencing times from weeks to mere hours. In five years, Stellar Innovations aims to become the invisible infrastructure behind industries that people rely on daily—without even realizing it.

Conclusion

From its origin as a small startup in Bangalore to a global leader in tech-led outsourcing, Stellar Innovations Pvt Ltd continues to break new ground under the leadership of CEO Anish Thomas. By staying ahead of technology trends and empowering its people, Stellar Innovations is not just scaling—it’s reshaping the future of multiple industries.

#Stellar Innovations CEO #Anish Thomas #Stellar Innovations CEO Anish Thomas #stellar innovations #stellar innovations bangalore #stellar innovations pvt ltd ceo

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wedesignyouny · 2 months ago

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VibeGTM vs. Vibe Coding: Revolutionizing Go-to-Market with Agentic AI

Introduction

Are we entering an era where AI “does the work” while we simply set the vision? For software developers, the answer is trending toward yes – thanks to vibe coding, a new paradigm where programmers let AI generate code from natural language prompts. Instead of painstakingly writing every line of code, developers using vibe coding “express their intention using plain speech” and let AI agents transform those ideas into executable software. The result? Coding projects can be spun up in a fraction of the time – in fact, AI models can produce code an order of magnitude faster than even expert human coders. This “forget that the code even exists” approach, as AI luminary Andrej Karpathy describes it, allows creators to focus on ideas and prototypes while the AI handles the heavy lifting.

Now, that same principle of effortless AI-driven execution is coming to the world of sales and marketing through VibeGTM. Just as vibe coding empowers developers to build software by simply describing what they want, VibeGTM (short for “vibe go-to-market”) enables business teams to launch full-fledged sales campaigns with minimal manual effort. Landbase – an AI technology company recognized as the leader in agentic AI for go-to-market (GTM) – is pioneering this approach as a core part of its strategy and technological positioning. The idea is straightforward: make GTM as easy as setting the vibe. Instead of weeks or months of planning, list building, and outreach, Landbase’s platform can suggest and orchestrate a multi-channel campaign in minutes. The user simply specifies high-level goals or target audiences, and the AI does the rest – much like a developer saying “build me a simple app that does X” and watching the AI generate it.

According to Salesforce research, 83% of sales teams using AI have seen revenue growth, versus 66% of teams without AI. Results like these underscore why trends like vibe coding and VibeGTM are gaining momentum – they promise to boost productivity and outcomes in fields that traditionally required intensive manual effort. In this blog, we’ll compare vibe coding and VibeGTM side by side, exploring how each works, the problems they solve, and what this means for professionals in telecom, commercial real estate, managed services, and beyond. We’ll see how Landbase’s agentic AI-powered VibeGTM approach is revolutionizing go-to-market execution, much as AI coding assistants have transformed software development. By the end, you’ll understand why Landbase positions itself as a category leader in this space and how embracing these innovations could give your business a strategic edge.

From Vibe Coding to VibeGTM: Two AI Revolutions, One Philosophy

Vibe coding and VibeGTM originate in very different domains – one in software engineering, the other in sales/marketing – yet they share a common philosophy: let AI handle the grunt work. Both emerged as responses to the question: what if we could achieve our goals by simply telling AI what we want, and letting it figure out the “how”?

Vibe Coding (software development): Coined by AI researcher Andrej Karpathy in early 2025, “vibe coding” is a fresh take on programming that puts AI at the forefront of writing code. Developers using this approach rely on AI coding assistants (powered by large language models) to generate and even debug code, while they guide the process with natural language prompts and high-level feedback. As IBM’s AI experts describe, vibe coding lets users “express their intention… and the AI transforms that thinking into executable code,” enabling a “code first, refine later” mindset. This means faster prototyping and iteration: one can build an application by simply describing features or changes (e.g. “make the login button blue and half the size”) and accepting the AI’s suggestions, without manually digging through code for every tweak. The payoff is tremendous speed and flexibility in development – early vibe coding adopters report launching weekend projects in hours instead of weeks, as the AI can produce functional code 10x faster than a human. Of course, a human remains in the loop for oversight and final polish (especially for production-quality software), but the heavy lifting of generating boilerplate, fixing minor bugs, and scaffolding entire modules is largely automated. Vibe coding, highlighted in major media from The New York Times to Ars Technica, has quickly gone from a niche term to a mainstream movement in programming – all within a few months of its inception.

VibeGTM (go-to-market execution): Coined by Landbase CEO Daniel Saks in early 2025, “vibe GTM” is inspired by the success of vibe coding. Landbase pioneered VibeGTM to bring a similar “AI does the work” experience to go-to-market strategy. In essence, VibeGTM is about using agentic AI to automate the complex, multi-step process of B2B sales outreach – from identifying target customers, to crafting personalized messages, to executing multi-channel campaigns. Rather than a sales team manually researching leads, writing emails, and following up tirelessly, VibeGTM envisions a world where a business user can say, for example, “Get me meetings with procurement managers in the top 50 healthcare companies in our region,” and the AI-powered system will handle everything needed to make it happen. Landbase’s CEO Daniel Saks explains that their latest product update – the Campaign Feed – “brings the fun and effortless experience of the ‘vibe coding’ phenomenon to GTM, making it easy to review, edit and launch campaigns in minutes instead of months.”. In practice, this means Landbase’s platform will recommend complete campaign strategies (audience selection, messaging, timing, channels) as easily as a coding AI suggests code changes. A user can review the suggested go-to-market campaign, tweak any details if needed, then launch it with one click – the AI takes care of executing the outreach across email, LinkedIn, phone, etc., and even monitors responses to optimize the next steps. This is made possible by Landbase’s proprietary AI engine, GTM-1 Omni, which is a domain-specific, multi-agent AI system purpose-built for sales and marketing workflows. Much like an AI pair-programmer in vibe coding, GTM-1 Omni acts as an “AI GTM team” that can design and run campaigns autonomously, while the human sets the high-level objectives.

At their core, both vibe coding and VibeGTM are about democratizing expertise through AI. Vibe coding allows even non-experts or time-strapped coders to create software by leveraging the AI’s knowledge of best practices and vast coding patterns. Similarly, VibeGTM allows a small business or a lean sales team to execute sophisticated marketing campaigns (traditionally requiring an army of SDRs, marketers, data researchers, and various tools) simply by tapping into Landbase’s AI, which carries the learned experience of thousands of successful campaigns. In both cases, AI acts as a force-multiplier for human creativity and strategic thinking: you focus on the “what” (the vision or goal), and the AI figures out the “how” (the execution steps). It’s a paradigm shift in how work gets done.

VibeGTM vs. Vibe Coding: Side-by-Side Comparison

How exactly do vibe coding and VibeGTM stack up against each other? Let’s compare these two AI-driven approaches across key dimensions:

As the comparison above shows, vibe coding and VibeGTM both empower users to achieve more with less effort – but they do so in different arenas. Vibe coding tackles the technical complexity of software creation, while VibeGTM addresses the operational complexity of scaling pipeline and sales outreach. Each lowers the barrier to entry in its field: you no longer need to be a veteran programmer to build a web app, and you no longer need a 20-person sales team to reach thousands of qualified prospects.

Importantly, both still benefit from human insight at the right moments. AI isn’t magically omniscient – a developer still must verify critical code, and a sales leader still sets the overall campaign strategy and ensures the messaging aligns with brand tone. But the time and effort saved are enormous. In software, this means more experiments and faster innovation. In GTM, it means more customer conversations and a fuller sales funnel without proportional headcount growth.

To illustrate, consider a telecom company using Landbase’s VibeGTM: traditionally, their sales team might spend weeks preparing outreach for a new product launch – compiling lists of businesses expanding to new locations, drafting emails about upgrading network services, ensuring compliance with telecom regulations. With Landbase, the AI can instantly identify, say, all multi-location businesses in the region that are growing (using real-time data signals), draft a tailored pitch about reliable connectivity for expansion, and ensure every message meets telecom compliance standards automatically. One Landbase telecom client added $400,000 in monthly recurring revenue during a slow season by having the AI find “hidden pockets of demand” and engage them at scale – something their human team alone struggled to do in that timeframe. This is the power of VibeGTM in action.

Meanwhile, software teams embracing vibe coding have similarly reported double-digit productivity boosts. A survey by HubSpot found that 47% of sales professionals (who often have to script emails or write reports) are already using generative AI tools like ChatGPT to help draft content – essentially a form of “vibe writing” – and 52% use AI to analyze data for decisions. Developers are doing the same with code: relying on AI for boilerplate allows them to focus on creative problem-solving. The trend is clear across industries: routine content generation (whether code or emails) is being offloaded to AI so humans can concentrate on strategy and relationships. In the next section, we’ll dive deeper into the specific pain points in GTM that VibeGTM solves, and how Landbase’s approach uniquely addresses them by building on the lessons from vibe coding.

Solving GTM Challenges with VibeGTM (Inspired by Vibe Coding’s Success)

Implementing a go-to-market strategy has historically been a resource-intensive endeavor. Let’s face it: traditional GTM execution is rife with challenges that drain time and money. This is precisely why an AI-driven solution like VibeGTM is so game-changing – it directly tackles these pain points. Many of the breakthroughs that made vibe coding appealing (automation of tedious tasks, real-time suggestions, learning from feedback) are now being applied to solve long-standing GTM headaches. Here are some key GTM challenges and how Landbase’s VibeGTM approach provides a solution:

Fragmented tools and data silos: Modern sales teams often juggle a patchwork of tools – a CRM for contacts, an email platform for outreach, LinkedIn for social selling, separate databases for leads, etc. Data ends up siloed, and reps waste time switching contexts. This fragmentation makes it hard to coordinate campaigns or get a unified view of what’s working. Landbase’s Solution: A single, unified AI platform that consolidates data and workflow. Landbase’s GTM-1 Omni acts as the central brain that integrates prospect data, engagement history, and campaign analytics. By replacing a “scatter” of point solutions with one intelligent system, Landbase ensures nothing falls through the cracks. Just as vibe coding tools integrate into your coding environment to provide on-the-fly help, Landbase’s platform integrates formerly disparate GTM functions into one seamless experience. The AI can then optimize holistically – for example, if email responses are low but LinkedIn messages get replies, the system shifts focus accordingly, something a human might miss if tools are disconnected. The result is a streamlined process where all moving parts of a campaign are orchestrated together. No more exporting lists from one system to import into another or manually reconciling metrics – the AI sees and manages the whole funnel in one place.

Time-intensive manual outreach: Prospecting and outreach can feel like a grind. Sales development reps (SDRs) might spend 70% of their day researching contacts, writing cold emails, and following up – leaving only a sliver of time for actual selling or learning about customers. This manual workload limits how many prospects a team can touch and slows down pipeline generation. Landbase’s Solution: Automation of repetitive tasks and 24/7 productivity. Landbase’s agentic AI essentially operates as an always-on SDR team that never sleeps. It can scour databases and the web to discover new leads, automatically generate personalized outreach, and send follow-ups at optimal times, all without human intervention. Early adopters of this AI outreach saw huge efficiency gains – one report noted Landbase’s system enabled companies to launch a full outbound program “in minutes rather than months”. In fact, Landbase estimates that using their platform can reduce the cost and effort of scaling a sales pipeline by 60–70% compared to hiring a traditional SDR team and piecemeal tools. Just as vibe coding saves developers from typing boilerplate code so they can focus on creative design, VibeGTM saves sales teams from drudgery (like piecing together lead info or writing yet another intro email) so they can focus on high-level strategy and closing deals. The AI handles the busywork of outreach at machine speed, sending potentially thousands of personalized touchpoints across multiple channels in the time it would take a human to manually send one batch of emails.

Low conversion from generic campaigns: “Spray and pray” emails and untargeted cold calls typically yield dismal results – prospects ignore messages that don’t speak to their needs. Many companies have seen their mass email campaigns lost in the noise, with meager reply rates and poor ROI. The problem is lack of personalization and relevance at scale; human teams often can’t customize every message deeply when contacting hundreds of leads. Landbase’s Solution: Hyper-personalization and continuous optimization using AI. This is where a domain-trained AI truly shines. Landbase’s model analyzes each prospect’s context (industry, role, company news, etc.) and tailors messaging accordingly. It’s trained on a vast dataset of what works – over 40 million sales email samples – so it crafts outreach with proven best practices for conversion. During early tests, this led to up to 7x better conversion rates versus standard one-size-fits-all emails. Think of it as the GTM equivalent of an AI coder knowing the optimal algorithm: the AI knows the optimal pitch for a given prospect. Moreover, Landbase employs a feedback loop akin to how vibe coding tools learn from user corrections. The platform tracks responses in real time and auto-tunes the campaign – if certain messaging resonates more or certain subject lines get better open rates, the AI adapts on the fly. This continuous learning is a hallmark of “agentic AI”: it not only executes tasks but also learns and improves from results. Humans alone would struggle to A/B test and iterate so rapidly at scale. Landbase’s AI essentially personalizes and optimizes every step automatically, ensuring each prospect interaction is as effective as possible. The outcome is significantly higher engagement and ROI from outreach efforts.

Scaling pipeline is costly and slow: If a company wants to dramatically increase its sales pipeline, the traditional playbook is to hire more SDRs, subscribe to more data services, and invest in more tooling – an approach that is expensive and can take months to ramp up. Hiring and training reps, for instance, might take 3-6 months before they are fully productive, and even then, their capacity is limited by working hours. Landbase’s Solution: On-demand scalability with AI at a fraction of the cost. Landbase offers what is essentially a scalable “AI SDR team” in the cloud. Need to double your outreach volume for a new product launch? Simply instruct the platform, and it can double the campaign outputs – no new hires required. Landbase has reported that companies using its platform can scale outreach at ~60% lower cost than scaling with human teams and traditional tools. This is because the AI handles more accounts simultaneously than a human ever could, and it doesn’t need benefits, office space, or sleep. One company executive described this as compressing a process that took months into minutes. In practical terms, a business can enter a new market or segment much faster. For example, a managed services provider (MSP) could traditionally only target a handful of industries at once due to limited sales staff. With Landbase, that MSP can launch tailored campaigns to multiple verticals in parallel – e.g., one campaign aimed at healthcare companies emphasizing compliance support, and another aimed at tech startups emphasizing agility – all driven by the same AI platform concurrently. This agility was unheard of before agentic AI. As a bonus, because the platform is subscription-based, companies move from high fixed labor costs to more flexible costs that scale with usage, improving efficiency. In one case, after implementing Landbase, a tech startup was able to significantly shorten its sales cycle by letting the AI rapidly zero in on the right audience and message, something that took them much longer before.

Knowledge and expertise gaps: Not every organization has top-tier sales ops experts or data scientists to optimize their go-to-market. A mid-sized commercial real estate firm, for instance, might not know the best practices to find tenants in a new market or what messaging yields responses from CFOs looking for office space. Similarly, an industrial supplier may not be adept at using intent data to time their outreach. Landbase’s Solution: Built-in expertise and best practices encoded in AI. Landbase’s agentic AI was developed by training on best-in-class sales workflows and copy – including input from veteran SDRs and marketing experts. It’s as if Landbase took the collective wisdom of dozens of high-performing sales teams and made it available on-demand through the platform. This means even a small team can execute like a seasoned pro. The AI “knows” which job titles are key decision-makers in different industries, what value propositions resonate in, say, telecom vs. finance, and even the optimal time of day to send an email to a VP-level contact. For example, Landbase’s knowledge graph and models understand that in telecom deals, emphasizing reliability and compliance is critical, whereas in commercial real estate outreach, referencing local market trends or expansion news might grab attention. The AI will automatically incorporate such insights. This flattens the learning curve for users – you don’t need a PhD in marketing to benefit; the AI provides suggestions and content that have a high likelihood of success out-of-the-box. In vibe coding terms, it’s like having an AI that already knows all common design patterns and pitfalls, so even a novice coder can produce decent software with its guidance. With Landbase, even a novice in GTM can run a solid campaign, because the agentic AI acts as an expert coach and executor in one. Moreover, Landbase’s team continues to update the AI (via their Applied AI Lab and continuous learning from all client campaigns), ensuring that the latest tactics and market shifts are reflected. This is crucial in fast-changing markets where what worked last quarter might not work now – the AI adapts faster than human training cycles.

In summary, VibeGTM directly addresses the pain points that have long frustrated sales and marketing professionals, using the same playbook that made vibe coding successful: automate the tedious stuff, augment human skill with AI insights, and iterate quickly based on data. The result is a solution-oriented, confident approach to GTM. Instead of being mired in operational logistics, teams can proactively strategize and engage with prospects who matter, leaving the rest to AI.

For professionals in industries like telecom, commercial real estate (CRE), and managed services, this is especially powerful. These sectors often involve complex B2B sales with long cycles and timing is everything – missing a single market trigger (like a company relocating offices, or a telecom client expanding infrastructure) can mean a lost deal. Landbase’s VibeGTM ensures you never miss a beat in the market. As soon as a relevant signal appears (e.g., a firm raises a new funding round or a tenant’s lease is up for renewal), the AI can pounce on it with tailored outreach, far faster than a human team could react. In a world where 76% of salespeople agree that by 2030 most people will use some form of AI or automation in their job(5), those who leverage VibeGTM will clearly have an edge in efficiency and effectiveness.

The Technology Behind the Scenes: How Vibe Coding and VibeGTM Leverage AI Differently

While vibe coding and VibeGTM share a vision of AI-driven ease, the underlying technologies are tuned to their respective domains. Understanding these differences can help decision-makers appreciate why a specialized platform like Landbase is needed for GTM, rather than trying to use a generic AI assistant.

Vibe coding’s tech stack: At the heart of vibe coding are large language models (LLMs) specialized in programming. Models like OpenAI’s Codex (which powers GitHub Copilot) and others (e.g., those behind Replit’s Ghostwriter or Cursor) have been trained on billions of lines of source code from public repositories. They effectively predict code given some context (like code that was already written, plus a developer’s prompt). Modern coding assistants also incorporate voice recognition (Karpathy mentions using voice input with “SuperWhisper” to talk to the AI and integrate with development environments to read the developer’s entire codebase for context. There’s also an element of agent behavior emerging – for example, if the code doesn’t compile, the assistant can read the error and automatically attempt a fix, looping until tests pass. This starts to resemble an “agentic” approach, but generally these tools are not fully autonomous; they react to the developer’s prompts or corrections. Importantly, vibe coding tools prioritize not breaking the flow: they give real-time suggestions as you code or converse, with the goal that the human can keep “in the zone” of creativity. The success of vibe coding thus far has relied on LLMs that are generalists in code (able to work across languages and frameworks), paired with a tight user interface loop that makes interacting with the AI quick and intuitive (e.g., hitting tab to accept a suggestion, or asking a question in natural language). As these models improve and perhaps incorporate more reinforcement learning from how developers use them, we might see even more autonomous coding agents. But currently, the developer is the orchestrator, and the AI is the savvy assistant.

VibeGTM’s tech stack (Landbase’s approach): Landbase’s GTM-1 Omni is a purpose-built AI specifically for go-to-market tasks, and this specialization is its strength. Instead of a single large model trying to do everything, Omni combines multiple AI components each optimized for a facet of the GTM process. According to Landbase, it integrates three types of AI capabilities into one system:

Predictive models – to analyze data signals and predict which prospects are likely to convert or which actions will yield the best results. For instance, predictive algorithms score leads based on thousands of intent signals (funding events, job postings, website visits, etc.) to prioritize outreach.

Generative models – to create content (emails, LinkedIn messages, call scripts) tailored to each situation. This includes natural language generation fine-tuned on successful sales communications. It’s not just general GPT-4 writing an email; it’s an AI trained on what a high-performing SDR would write when reaching out to, say, a VP of Finance in the SaaS industry, including appropriate terminology and pain points.

Action models – to execute tasks across systems, meaning the AI can actually send emails, schedule calendar invites, update CRM entries, etc., via API integrations, without needing a human to press the button. This is where agentic AI comes in – the system can act autonomously in digital environments (email servers, CRM, social networks) to carry out the steps of the campaign.

These components are orchestrated by an agentic framework that understands objectives, not just instructions. As Landbase’s team explains, unlike a typical AI assistant that only responds to direct prompts (“write an email about X”), an agentic AI can take a higher-level goal (“generate pipeline in healthcare sector”) and break it down into sub-tasks – identify healthcare companies, find relevant contacts, craft messages, send, follow-up, and so on – adjusting along the way. This is analogous to having an AI project manager combined with AI workers for each task. Under the hood, Landbase’s platform is also powered by a massive proprietary dataset: a knowledge graph of over 220 million contacts and 40 million sales interactions feeds the AI’s understanding of business relationships and language. This is a stark contrast to generic models like ChatGPT which, while trained on a broad swath of the internet, don’t have up-to-date or deep proprietary sales data and often have to be manually given context about a company or market. Landbase’s system already knows a lot about a given industry or account from its data, so it can proactively use that context in campaigns.

Another key tech difference is continuous learning and optimization. Landbase’s agentic AI doesn’t stop at sending messages – it monitors what happens next (did the prospect open the email? click a link? reply with interest? ignore it?) and feeds that outcome back into its models to learn and improve. It’s akin to how a self-driving car AI learns from each mile driven. Over time, the system becomes more and more effective for each user and in each domain. Traditional vibe coding assistants also learn (e.g., Copilot refines suggestions based on your codebase), but the learning is narrower (mainly about code style, not outcomes in the world). Landbase’s AI is learning what business strategies bear fruit.

For decision-makers, the implication is that while a general AI like GPT-4 could theoretically write a sales email if prompted, it’s not enough to run a full VibeGTM motion. Landbase’s technology advantage lies in integrating the full stack of GTM tasks with an AI that has domain expertise and can take actions autonomously. This is not trivial to build from scratch. It’s why Landbase, founded in 2024 by experienced entrepreneurs (Daniel Saks, co-founder of AppDirect), invested in being the first mover with an agentic GTM model – carving out a technological lead that is hard for others to replicate quickly. They effectively built a vertical AI solution, whereas vibe coding largely leverages horizontal AI tools.

From a strategic standpoint, using Landbase’s VibeGTM is more comparable to hiring an AI-powered consultancy than using a simple tool. It’s a holistic system. This is reflected in how Landbase goes to market as well – they have an Agentic AI Lab to keep advancing the tech and even an agency network to help clients succeed with the platform(1). They recognize that technology adoption in GTM isn’t just plug-and-play; often some change management and expertise helps. This is different from vibe coding tools, which are usually self-serve and purely product-led (developers just install a plugin). The extra layer of support Landbase provides (blending human expertise and AI, as they emphasize) indicates that VibeGTM technology, while powerful, is deployed in partnership with businesses to reshape their processes, not just as a casual assistant.

In short, vibe coding and VibeGTM both use cutting-edge AI, but one size does not fit all. Vibe coding rides on the coattails of general LLMs trained on code and is delivered as lightweight tools for devs. VibeGTM runs on a purpose-built, multi-agent AI ensemble trained on sales data and is delivered as an enterprise-grade platform. For companies evaluating solutions, understanding this difference is key. If you try to use a generic chatbot to do your GTM, you’ll likely hit a ceiling – it won’t know your context deeply, and it can’t take autonomous actions safely. Landbase’s VibeGTM, on the other hand, was engineered to be your GTM co-pilot from day one, with guardrails, data, and integrations needed for real business use.

Embracing the Vibe: How to Leverage Vibe Coding and VibeGTM in Your Business

By now, it’s clear that both vibe coding and VibeGTM represent a significant shift in how work gets done – shifting many tasks from humans to AI, and freeing up humans to do higher-value thinking. The question for business leaders and professionals is: How can we practically embrace these trends to stay ahead?

1. Start with a pilot in a low-risk area. Whether it’s coding or GTM, a prudent first step is to experiment. For vibe coding, this might mean allowing your software team to use AI assistants on an internal tool or non-critical project to get familiar with the workflow. For VibeGTM, you could identify a particular campaign or segment that’s not core to your revenue and let Landbase’s AI run with it as a trial. Treat it as an A/B test: AI-driven campaign vs. your normal process. Early pilots often build confidence. For example, one sales team might test Landbase on a dormant lead list that hadn’t been touched in a year – if the AI manages to revive some of those leads with clever emails, that’s immediate proof of value. Keep the scope defined, measure results, and iterate. Many companies find that initial successes make it easier to get buy-in for broader AI adoption.

2. Educate and involve your team. A common misconception is that AI will replace humans wholesale. In reality – and this has been echoed by early vibe coding practitioners – the best outcomes come when humans collaborate with AI, not just turn it on blindly. Make sure your team understands that vibe coding or VibeGTM are tools to enhance their capabilities, not threats to their jobs. Involve your sales reps in the process of refining AI-generated content; their feedback about message tone or customer pain points will train the AI to be even better for your specific context. Similarly, developers should review and learn from AI-written code – it can actually be a learning opportunity to see how the AI approaches problems. By fostering a mindset of AI as a teammate, you’ll reduce resistance and get more value. At Landbase’s clients, for instance, SDRs who initially feared being replaced ended up appreciating that the AI took over the drudgery, allowing them to focus on live conversations where their skills shine. It’s worth noting that 97% of business owners believe ChatGPT (and by extension AI tools) will help their business, but employees need to see it helping them personally. So highlight wins like, “AI saved you 5 hours this week – now you can spend that time closing deals or building client relationships.”

3. Leverage data – yours and external – to maximize AI effectiveness. VibeGTM works best when it has rich data to chew on. Internally, integrate your CRM and marketing data with the platform so it learns from your history (Landbase can use a company’s past email performance, for example, to tailor its model). Externally, take advantage of the data Landbase provides (their massive B2B contact database and intent signals). A telecom firm might feed in its customer profiles and let the AI find lookalikes in Landbase’s database, using criteria a human might not think of. The more the AI knows, the better the vibes it can operate on. For vibe coding, feeding your codebase or style guides to the AI can help it align with your standards – for instance, tools allow you to provide example code or tests so the AI writes code that passes them. Essentially, don’t treat these AI solutions as black boxes; treat them as adaptive systems that you can train with the right data for superior results. Landbase’s platform, for one, allows customization and learning based on your feedback – utilize that by consistently tagging what a “good” vs “bad” lead or email looks like for you, so the AI gets smarter in targeting and messaging.

4. Monitor, measure, and maintain oversight. Even after you adopt VibeGTM, keep humans in the loop for oversight, especially in the initial stages. This is not because the AI is likely to go rogue, but because nuances in business (or code) sometimes require a judgment call. For sales, ensure someone reviews the AI’s messaging periodically for brand alignment and checks that the campaign outcomes align with quality leads (not just quantity). You might set up a dashboard to track open rates, reply rates, conversion rates of AI-led campaigns vs. historical benchmarks – Landbase’s analytics can help with that. If something seems off (e.g., a particular sequence underperforming), the team can adjust parameters or provide feedback to the AI. Essentially, treat the AI as an apprentice – capable of doing a lot, but still benefiting from mentorship. Over time, as trust builds and the AI consistently meets or exceeds targets, you can dial back the level of human review. Many Landbase customers find that after a few months, the AI’s suggestions are so on-point that they rarely need to edit outreach content – they shift to focusing on the increase in meetings and deals coming from the campaigns. Similarly, developers who use vibe coding tools often start by double-checking every AI-generated line, but soon learn to trust the AI for routine tasks and only deeply review the critical logic. The goal is a calibrated balance where human oversight is there, but not a bottleneck.

5. Address compliance and ethics proactively. With AI taking on actions like sending emails or generating content, ensure you have guidelines to prevent any mishaps. Landbase’s system has built-in compliance checks (for opt-outs, regional regulations like GDPR, etc.) – make sure those are configured for your needs. For example, a commercial real estate outreach might need to avoid certain statements that could be seen as investment advice; those should be communicated to the Landbase team so the AI can be tuned accordingly. In vibe coding, if your company has policies on open-source licensing or code security, ensure the AI suggestions are vetted for compliance with those (e.g., avoid copying large chunks of code from unknown sources). The good news is that AI can actually enhance compliance – Landbase’s AI, for instance, automatically manages opt-out lists and respects communication preferences, reducing the risk of human error in sending an email to someone who unsubscribed. It even keeps messaging on-script to avoid unapproved claims. But it’s important to set these guardrails early. Engage your legal or compliance team in reviewing the AI’s approach. This builds confidence that AI isn’t a wildcard but a controlled, strategic asset.

By following the above steps, organizations in telecom, CRE, managed services, and beyond can gradually and successfully integrate both vibe coding and VibeGTM into their operations. The benefits – from faster time-to-market, to cost savings, to higher conversion rates – are too significant to ignore. As one study highlighted, 81% of sales teams are either experimenting with or fully using AI in some capacity already(4). Those who haven’t started risk falling behind competitors who can engage customers faster and more effectively with AI’s help. The same goes for software development: teams not leveraging AI may struggle to match the rapid iteration and output of those that do.

The era of AI-augmented work is here. The “vibe” revolution, whether in coding or go-to-market, is all about reclaiming our time and focusing on what humans do best – creativity, strategy, relationship-building – while delegating the rest to capable AI agents. As Andrej Karpathy humorously noted, vibe coding can feel like you “barely even touch the keyboard” and just let ideas flow(3). Landbase’s vision for VibeGTM is analogous: you barely have to touch the tedious parts of GTM execution, you just set the direction and watch pipeline flow. Companies that embrace this mindset stand to unlock unprecedented efficiency and agility.

Conclusion: Embrace the VibeGTM Revolution in GTM Strategy

Both vibe coding and VibeGTM highlight a transformative truth: when humans and AI collaborate, the sum is far greater than the parts. Developers have learned that by embracing AI “co-pilots,” they can ship software faster and focus on innovation. Now, sales and marketing leaders are discovering that by harnessing an agentic AI like Landbase’s VibeGTM, they can supercharge their go-to-market execution and focus on strategic growth initiatives rather than manual toil.

For professionals in telecom, commercial real estate, managed services, or any sector that runs on B2B relationships, the message is clear: Don’t get left behind in the AI-driven GTM wave. Early adopters are already seeing outsized gains – more leads, faster deal cycles, and lower costs. The comparison between vibe coding and VibeGTM isn’t just academic; it’s a roadmap for how work is evolving. Just as writing code by hand is giving way to guiding code with AI, the days of building pipeline solely through human effort are numbered. The future of GTM is agentic, intelligent, and incredibly efficient.

Landbase, with its first-of-its-kind agentic AI platform for GTM, is positioning itself as the category leader in this new era of go-to-market. The company not only provides powerful technology but has demonstrated a commitment to customer success (through its AI lab, support network, and continuous innovation)(1). This means when you partner with Landbase, you’re not just buying software – you’re gaining a cutting-edge AI team member that’s constantly improving and working tirelessly for your revenue goals.

Ready to experience the power of VibeGTM for yourself? Now is the time to take action. Start by exploring how Landbase’s agentic AI platform can fit into your organization’s GTM strategy. Consider running a pilot campaign and see the results firsthand – the numbers and ROI will speak louder than any words. As the saying goes, the best way to predict the future is to create it. By adopting VibeGTM, you’re not only predicting the future of sales and marketing – you’re actively creating a future where your go-to-market engine is smarter, faster, and more adaptable than ever before.

Embrace the VibeGTM revolution and put your go-to-market on autopilot. Landbase is here to help you lead this change, with confidence, as a true category leader in AI-powered GTM. Don’t work for your software – let your software work for you, so you can reclaim your day and do more of what you love. It’s time to let AI set the vibe for unprecedented growth.

#vibegtm #Agentic AI California #AI agents California #AI SDR California #Go-to-market California #ai go to market

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christianbale121 · 2 months ago

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Can AI Code Generation Software Development Replace Manual Coding in Enterprise-Grade Software Projects Without Compromising Quality?

As enterprise software grows more complex, the need for faster, error-free development has never been greater. Enter AI code generation software—a rapidly advancing technology that automates parts of the coding process using machine learning and large language models. But the big question remains: Can AI truly replace manual coding in enterprise-grade projects without compromising quality? This blog explores the strengths, limitations, and future of AI in enterprise software development.

What Is AI Code Generation Software?

AI code generation software uses machine learning algorithms, particularly large language models (LLMs), to analyze context and generate executable code. These tools can write boilerplate code, suggest completions, refactor logic, and even fix bugs. Examples include GitHub Copilot, Amazon CodeWhisperer, and various custom-trained enterprise AI models.

Benefits of AI Code Generation in Enterprise Projects

1. Accelerated Development Cycles

AI tools reduce the time spent writing repetitive code, allowing developers to focus on architecture and business logic. This results in faster product releases and improved agility.

2. Improved Code Consistency

By suggesting standardized patterns, AI helps maintain consistency across large codebases—crucial for enterprise teams managing thousands of lines of code.

3. Error Detection and Reduction

AI can flag syntax issues, logical flaws, and security vulnerabilities early, decreasing the cost and time spent on debugging.

4. Enhanced Developer Productivity

Developers can offload routine tasks like writing CRUD operations, generating test cases, or translating pseudo code into real code—boosting productivity and morale.

5. Faster Onboarding for New Developers

AI assistants act like real-time mentors, helping junior developers understand existing code and best practices within the enterprise environment.

Where AI Code Generation Falls Short

1. Context Awareness Limitations

Enterprise applications often require deep domain-specific knowledge. AI lacks true contextual understanding of nuanced business rules, which can lead to inaccurate or irrelevant suggestions.

2. Architectural Decision-Making

AI is great at producing code snippets, but it can't yet design high-level system architecture, make trade-off decisions, or understand legacy systems intricately.

3. Security and Compliance Risks

AI-generated code might inadvertently introduce security flaws or fail to comply with industry-specific regulations (like HIPAA or GDPR), especially if not properly validated.

4. Code Quality Assurance

While AI can assist with testing, human oversight is essential to validate correctness, performance, and maintainability—particularly in mission-critical applications.

Real-World Use Cases of AI Code Generation

Financial Services: Automating the generation of regulatory reports with embedded validation logic.

Healthcare: Assisting with EMR system enhancements by generating templates for new forms or APIs.

E-commerce: Speeding up backend development with auto-generated inventory and order management modules.

Telecom: Streamlining network configuration tools by generating interfaces and automation scripts.

Can AI Fully Replace Manual Coding?

Not yet. While AI code generation software can significantly augment development processes, complete replacement is not viable for enterprise-grade projects. The stakes are too high when dealing with critical systems that must scale, secure user data, and comply with strict regulations.

Instead, AI acts as a collaborative partner—automating the mundane, supporting the complex, and enabling developers to build better software, faster.

The Future: Human-AI Collaboration

The future of enterprise development lies in hybrid workflows where human expertise and AI efficiency converge. Developers will increasingly become code curators and architects, guiding AI to ensure quality and compliance while letting machines handle the grunt work.

Conclusion

AI code generation software development is reshaping how enterprises build software—improving speed, quality, and productivity. However, replacing manual coding entirely in enterprise-grade projects is still out of reach. The best outcomes come from leveraging AI as an assistant, not a replacement.

As this technology matures, organizations that adopt a human-in-the-loop model will reap the greatest benefits—achieving scale without sacrificing code quality or business integrity.

#ai

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iotexpo · 2 months ago

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[IOTE2025 Shanghai Exhibitor] Shandong Yunze-This company's revenue increased by 43.57% in 2024

IOTE 2025, the 23rd International Internet of Things Exhibition Shanghai, will be held in Shanghai New International Expo Center from June 18 to 20, 2025. This time, it will join hands with MWC Shanghai to write a new chapter in the Internet of Things and mobile communications!

Shandong Yunze Information Technology Co., Ltd. will participate in this exhibition. Before the opening, we interviewed the company to give you a sneak peek at what Shandong Yunze will bring to the exhibition.

Exhibitor Introduction

Shandong Yunze Information Technology Co., Ltd.

Booth No.: N5A70

June 18-20, 2025

Shanghai New International Expo Center

Exhibitor Interview

Developing new quality productivity is an inherent requirement and an important focus for promoting high-quality development. This year's government work report clearly proposed "developing new quality productivity in accordance with local conditions and accelerating the construction of a modern industrial system". How do you understand the relationship between the Internet of Things and new quality productivity? What practices does the company have in promoting intelligent production and collaborative innovation in the industrial chain?

As one of the integrated carriers of the new generation of information technology, the Internet of Things drives the development of new quality productivity through three core capabilities: First, the intelligent perception system relies on the sensor network to realize real-time data collection in the entire domain and build an accurate digital mirror of the physical world; second, the data interconnection mechanism breaks the information island of traditional industries and realizes the global scheduling and collaborative optimization of production factors through cross-system protocol standardization; third, the real-time response capability uses edge computing and AI decision-making models to form a closed-loop control of "perception-analysis-execution" and dynamically adapt to changes in the production environment. The synergy of these three characteristics is reshaping the production paradigm.

At present, AI big models are developing rapidly, which has a profound impact and positive driving effect on the development of the Internet of Things. How do you view the development of technologies such as AI big models? What layout and plans does the company have in this regard? Please give an example.

The rapid development of AI big models has injected new momentum into the field of the Internet of Things. Its core value lies in promoting the transition of devices from "connection" to "intelligence" through efficient processing of heterogeneous data and intelligent decision-making. Taking voice interaction as an example, the big model not only improves the accuracy of semantic understanding, but also realizes natural dialogue through context association, which is the key to IoT devices breaking through traditional command-based interaction. In the future, the deep integration of AI and the Internet of Things will accelerate the experience upgrade of scenarios such as smart homes and industrial Internet, and at the same time give birth to innovative models of edge computing and cloud-edge collaboration.

What products and solutions will Shandong Yunze bring to this exhibition?

(1) IoT modules: We will bring NB module series such as MN316 and MN328; 4G module series such as ML307A, ML307R, ML307N, ML307C, ML307M, ML307H promoted by China Mobile OneMO, Air780 module and Air724 of Hezhou; 5G module series such as MR880A, MR880Q, MF870M, MF871, MF309, MF310; vehicle-mounted module series such as ML551Z-SL and MF572E-BC;

(2) IoT tariffs: Mainly gather resources from the four major domestic operators, China Mobile, China Telecom, China Unicom and China Broadcasting Corporation. The business is mainly IoT access services, covering traffic package customization, traffic management, VPDN private network and other services. Provide customers with flexible tariff strategies to support users' communication resource needs with different usage; provide diversified tariff structures suitable for business scenarios of different types of users.

(3) Consumer-grade terminals: 4G all-network CPE, 4G all-network UIFI, 4G all-network CPE (WIFI6), etc.; shared products include shared charging piles, etc.; fire protection and security products mainly include door magnetic detectors, emergency buttons, smoke detectors, gas detectors, water detectors, temperature and humidity detectors, carbon monoxide detectors and other detectors, as well as CAT1 smart badges, smart watches, NB power failure alarms, gas alarms, NB gateway alarms, AI voice remote controls, surveillance cameras, etc.

(4) Industrial-grade terminals: mainly include DTU, 4G/5G industrial-grade routers, 4G/5G industrial-grade smart gateways, edge computing and smart terminals. Among them, industrial routers include dual-port industrial routers, multi-port industrial routers, single-mode dual-card industrial routers and other types; 4G/5G industrial smart gateways include industrial energy monitoring gateways, smart light pole dedicated gateways, smart gateways, etc.; smart terminals include electronic energy meters, offline scenario marketing smart screens, etc.

(5) Internet of Things Integrated Business Support Platform (IoTLink V2.0): supports the integrated management of IoT cards, IoT modules and card + module, and open source code. It provides functions such as data collection and data analysis. There are mainly pages such as card information, card list display, device list display, tariff group display, tariff display, product display, inventory management, etc.

(6) Internet of Things Solutions: mainly brings smart solutions such as smart agriculture, smart communities, smart factories, smart parks, and public utility remote meter reading.

At present, industry trends are changing rapidly, and it is crucial to seize opportunities and seek cooperation. Here, we sincerely invite you to participate in the IOTE 2025 23rd International Internet of Things Exhibition Shanghai Station held at Hall N5 of Shanghai New International Expo Center from June 18 to 20, 2025. By then, you are welcome to visit Shandong Yunze N5A70 in Hall N5 to discuss the cutting-edge trends and development directions of the industry with us, explore cooperation opportunities, and look forward to your visit!

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tech4bizsolutions · 3 months ago

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Smarter VLSI Design Through AI-Powered Innovation

As semiconductor technology continues to evolve, the demand for high-performance and energy-efficient chips is reshaping how integrated circuits are designed. Traditional VLSI (Very-Large-Scale Integration) methods, while foundational, are becoming insufficient for the scale and complexity of modern electronics. To bridge this gap, Artificial Intelligence (AI) is being integrated into VLSI design workflows — unlocking smarter, faster, and more optimized chip development.

Tech4BizSolutions is actively leveraging AI to enhance every stage of the VLSI lifecycle, from architecture planning to post-silicon validation.

What Is AI-Driven VLSI Design?

AI-driven VLSI design refers to the application of machine learning, deep learning, and data analytics to automate and optimize various stages of chip design and manufacturing. Unlike conventional design flows, AI can identify patterns, predict outcomes, and generate insights from massive datasets in real-time.

Key advantages include:

Improved performance-to-power ratio

Reduced manual effort in layout planning

Faster design rule checking and validation

Enhanced yield prediction and fault analysis

Tech4BizSolutions integrates AI across multiple design layers, reducing time-to-market while improving design accuracy and production reliability.

How Tech4BizSolutions Enhances VLSI with AI

At Tech4BizSolutions, we fuse our deep domain knowledge in semiconductor design with cutting-edge AI techniques. Here’s a breakdown of how we apply AI to make VLSI smarter:

1. AI in Design Space Exploration

Design space exploration is one of the most time-consuming phases of VLSI. Our AI models intelligently evaluate thousands of possible configurations, identifying the most efficient architecture with minimal resource usage.

Tech4BizSolutions Result: Up to 50% reduction in time spent exploring design alternatives.

2. Automated Floorplanning and Layout Optimization

Floorplanning and placement affect timing, area, and power consumption. We use neural networks to predict optimal component placement and signal routing paths, reducing congestion and delay.

Tech4BizSolutions Advantage: Increased chip efficiency with reduced layout iterations.

3. AI-Enhanced Timing and Power Analysis

Tech4BizSolutions uses AI models trained on historical data to predict timing violations and power bottlenecks before physical implementation. This allows early-stage corrections, saving time and silicon costs.

Outcome: More accurate PPA (Performance, Power, Area) metrics at the RTL level.

4. Fault Detection and Yield Improvement

AI helps detect subtle, non-obvious design flaws by recognizing patterns in simulation and test bench outputs. We also use AI to simulate rare corner cases that are typically missed in standard verification cycles.

Business Impact: Higher first-pass silicon success rates and lower manufacturing risks.

5. Adaptive Learning Systems for Continuous Optimization

Our AI systems are not static. They learn and evolve with every project. We build feedback loops where post-silicon data refines future simulations and models — creating a smarter design pipeline over time.

Long-term Benefit: Each new chip design becomes more efficient than the last, reducing NRE (Non-Recurring Engineering) costs.

Tech4BizSolutions: Delivering Tangible Business Value

By embedding AI into VLSI design workflows, Tech4BizSolutions helps clients:

Speed up development cycles by up to 40%

Reduce power consumption by designing for energy-aware applications

Increase IC performance through AI-informed microarchitecture tuning

Minimize silicon iterations and time spent on debugging

Predict and eliminate faults before tape-out

This makes our approach ideal for industries like:

Consumer Electronics

Automotive & EV

Industrial Automation

Telecom and 5G

IoT and Edge Devices

Real-World Use Case

Let’s say a client needs a custom AI accelerator chip for real-time video processing. With traditional VLSI design, modeling workloads, optimizing for latency, and reducing power draw can take months.

With Tech4BizSolutions’ AI-enhanced VLSI flow, we:

Use AI models to simulate expected video processing loads

Automatically adjust component placement for thermal efficiency

Predict the power envelope across real-world scenarios

Validate logic paths using AI-driven test vectors

Result: A custom ASIC delivered 30% faster with optimized performance and reliability.

Conclusion: The Future of Smarter Chip Design Starts Here

VLSI design is undergoing a significant transformation. As chip complexity continues to rise, integrating AI into every stage of the design and manufacturing process is not just an option — it’s a necessity.

Tech4BizSolutions is proud to lead this evolution with intelligent VLSI design solutions that are adaptive, efficient, and future-ready. Our AI-infused approach ensures not only faster development but smarter chips that can meet the demands of modern, connected, and data-driven applications.

Whether you’re building the next-gen smart device or need custom silicon for industrial systems, Tech4BizSolutions has the tools, talent, and technology to deliver.

Want to learn how AI can power your next chip design?

Contact Tech4BizSolutions today and explore the possibilities of intelligent VLSI.

#VLSIDesign #AIDrivenDesign #SemiconductorInnovation #ChipDesign #Tech4BizSolutions #HardwareInnovation #ASICDesign #EDAtools #DesignAutomation #TechSolutions #PowerEfficientDesign #CustomChipDesign

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sunaleisocial · 5 months ago

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Introducing the MIT Generative AI Impact Consortium

New Post has been published on https://sunalei.org/news/introducing-the-mit-generative-ai-impact-consortium/

Introducing the MIT Generative AI Impact Consortium

From crafting complex code to revolutionizing the hiring process, generative artificial intelligence is reshaping industries faster than ever before — pushing the boundaries of creativity, productivity, and collaboration across countless domains.

Enter the MIT Generative AI Impact Consortium, a collaboration between industry leaders and MIT’s top minds. As MIT President Sally Kornbluth highlighted last year, the Institute is poised to address the societal impacts of generative AI through bold collaborations. Building on this momentum and established through MIT’s Generative AI Week and impact papers, the consortium aims to harness AI’s transformative power for societal good, tackling challenges before they shape the future in unintended ways.

“Generative AI and large language models [LLMs] are reshaping everything, with applications stretching across diverse sectors,” says Anantha Chandrakasan, dean of the School of Engineering and MIT’s chief innovation and strategy officer, who leads the consortium. “As we push forward with newer and more efficient models, MIT is committed to guiding their development and impact on the world.”

Chandrakasan adds that the consortium’s vision is rooted in MIT’s core mission. “I am thrilled and honored to help advance one of President Kornbluth’s strategic priorities around artificial intelligence,” he says. “This initiative is uniquely MIT — it thrives on breaking down barriers, bringing together disciplines, and partnering with industry to create real, lasting impact. The collaborations ahead are something we’re truly excited about.”

Developing the blueprint for generative AI’s next leap

The consortium is guided by three pivotal questions, framed by Daniel Huttenlocher, dean of the MIT Schwarzman College of Computing and co-chair of the GenAI Dean’s oversight group, that go beyond AI’s technical capabilities and into its potential to transform industries and lives:

How can AI-human collaboration create outcomes that neither could achieve alone?

What is the dynamic between AI systems and human behavior, and how do we maximize the benefits while steering clear of risks?

How can interdisciplinary research guide the development of better, safer AI technologies that improve human life?

Generative AI continues to advance at lightning speed, but its future depends on building a solid foundation. “Everybody recognizes that large language models will transform entire industries, but there’s no strong foundation yet around design principles,” says Tim Kraska, associate professor of electrical engineering and computer science in the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) and co-faculty director of the consortium.

“Now is a perfect time to look at the fundamentals — the building blocks that will make generative AI more effective and safer to use,” adds Kraska.

“What excites me is that this consortium isn’t just academic research for the distant future — we’re working on problems where our timelines align with industry needs, driving meaningful progress in real time,” says Vivek F. Farias, the Patrick J. McGovern (1959) Professor at the MIT Sloan School of Management, and co-faculty director of the consortium.

A “perfect match” of academia and industry

At the heart of the Generative AI Impact Consortium are six founding members: Analog Devices, The Coca-Cola Co., OpenAI, Tata Group, SK Telecom, and TWG Global. Together, they will work hand-in-hand with MIT researchers to accelerate breakthroughs and address industry-shaping problems.

The consortium taps into MIT’s expertise, working across schools and disciplines — led by MIT’s Office of Innovation and Strategy, in collaboration with the MIT Schwarzman College of Computing and all five of MIT’s schools.

“This initiative is the ideal bridge between academia and industry,” says Chandrakasan. “With companies spanning diverse sectors, the consortium brings together real-world challenges, data, and expertise. MIT researchers will dive into these problems to develop cutting-edge models and applications into these different domains.”

Industry partners: Collaborating on AI’s evolution

At the core of the consortium’s mission is collaboration — bringing MIT researchers and industry partners together to unlock generative AI’s potential while ensuring its benefits are felt across society.

Among the founding members is OpenAI, the creator of the generative AI chatbot ChatGPT.

“This type of collaboration between academics, practitioners, and labs is key to ensuring that generative AI evolves in ways that meaningfully benefit society,” says Anna Makanju, vice president of global impact at OpenAI, adding that OpenAI “is eager to work alongside MIT’s Generative AI Consortium to bridge the gap between cutting-edge AI research and the real-world expertise of diverse industries.”

The Coca-Cola Co. recognizes an opportunity to leverage AI innovation on a global scale. “We see a tremendous opportunity to innovate at the speed of AI and, leveraging The Coca-Cola Company’s global footprint, make these cutting-edge solutions accessible to everyone,” says Pratik Thakar, global vice president and head of generative AI. “Both MIT and The Coca-Cola Company are deeply committed to innovation, while also placing equal emphasis on the legally and ethically responsible development and use of technology.”

For TWG Global, the consortium offers the ideal environment to share knowledge and drive advancements. “The strength of the consortium is its unique combination of industry leaders and academia, which fosters the exchange of valuable lessons, technological advancements, and access to pioneering research,” says Drew Cukor, head of data and artificial intelligence transformation. Cukor adds that TWG Global “is keen to share its insights and actively engage with leading executives and academics to gain a broader perspective of how others are configuring and adopting AI, which is why we believe in the work of the consortium.”

The Tata Group views the collaboration as a platform to address some of AI’s most pressing challenges. “The consortium enables Tata to collaborate, share knowledge, and collectively shape the future of generative AI, particularly in addressing urgent challenges such as ethical considerations, data privacy, and algorithmic biases,” says Aparna Ganesh, vice president of Tata Sons Ltd.

Similarly, SK Telecom sees its involvement as a launchpad for growth and innovation. Suk-geun (SG) Chung, SK Telecom executive vice president and chief AI global officer, explains, “Joining the consortium presents a significant opportunity for SK Telecom to enhance its AI competitiveness in core business areas, including AI agents, AI semiconductors, data centers (AIDC), and physical AI,” says Chung. “By collaborating with MIT and leveraging the SK AI R&D Center as a technology control tower, we aim to forecast next-generation generative AI technology trends, propose innovative business models, and drive commercialization through academic-industrial collaboration.”

Alan Lee, chief technology officer of Analog Devices (ADI), highlights how the consortium bridges key knowledge gaps for both his company and the industry at large. “ADI can’t hire a world-leading expert in every single corner case, but the consortium will enable us to access top MIT researchers and get them involved in addressing problems we care about, as we also work together with others in the industry towards common goals,” he says.

The consortium will host interactive workshops and discussions to identify and prioritize challenges. “It’s going to be a two-way conversation, with the faculty coming together with industry partners, but also industry partners talking with each other,” says Georgia Perakis, the John C Head III Dean (Interim) of the MIT Sloan School of Management and professor of operations management, operations research and statistics, who serves alongside Huttenlocher as co-chair of the GenAI Dean’s oversight group.

Preparing for the AI-enabled workforce of the future

With AI poised to disrupt industries and create new opportunities, one of the consortium’s core goals is to guide that change in a way that benefits both businesses and society.

“When the first commercial digital computers were introduced [the UNIVAC was delivered to the U.S. Census Bureau in 1951], people were worried about losing their jobs,” says Kraska. “And yes, jobs like large-scale, manual data entry clerks and human ‘computers,’ people tasked with doing manual calculations, largely disappeared over time. But the people impacted by those first computers were trained to do other jobs.”

The consortium aims to play a key role in preparing the workforce of tomorrow by educating global business leaders and employees on generative AI evolving uses and applications. With the pace of innovation accelerating, leaders face a flood of information and uncertainty.

“When it comes to educating leaders about generative AI, it’s about helping them navigate the complexity of the space right now, because there’s so much hype and hundreds of papers published daily,” says Kraska. “The hard part is understanding which developments could actually have a chance of changing the field and which are just tiny improvements. There’s a kind of FOMO [fear of missing out] for leaders that we can help reduce.”

Defining success: Shared goals for generative AI impact

Success within the initiative is defined by shared progress, open innovation, and mutual growth. “Consortium participants recognize, I think, that when I share my ideas with you, and you share your ideas with me, we’re both fundamentally better off,” explains Farias. “Progress on generative AI is not zero-sum, so it makes sense for this to be an open-source initiative.”

While participants may approach success from different angles, they share a common goal of advancing generative AI for broad societal benefit. “There will be many success metrics,” says Perakis. “We’ll educate students, who will be networking with companies. Companies will come together and learn from each other. Business leaders will come to MIT and have discussions that will help all of us, not just the leaders themselves.”

For Analog Devices’ Alan Lee, success is measured in tangible improvements that drive efficiency and product innovation: “For us at ADI, it’s a better, faster quality of experience for our customers, and that could mean better products. It could mean faster design cycles, faster verification cycles, and faster tuning of equipment that we already have or that we’re going to develop for the future. But beyond that, we want to help the world be a better, more efficient place.”

Ganesh highlights success through the lens of real-world application. “Success will also be defined by accelerating AI adoption within Tata companies, generating actionable knowledge that can be applied in real-world scenarios, and delivering significant advantages to our customers and stakeholders,” she says.

Generative AI is no longer confined to isolated research labs — it’s driving innovation across industries and disciplines. At MIT, the technology has become a campus-wide priority, connecting researchers, students, and industry leaders to solve complex challenges and uncover new opportunities. “It’s truly an MIT initiative,” says Farias, “one that’s much larger than any individual or department on campus.”

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govindhtech · 1 year ago

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How Hybrid AI Makes Smaller Language Models(SLMs) Powerful

As large language models (LLMs) have become widely used, users have learned how to utilize the apps that access them. These days, artificial intelligence systems can produce, synthesize, translate, categorize, and even talk. After learning from preexisting artefacts, IBM can create replies to prompts using tools in the generative AI field.

The edge and limited device space is one that has not seen a lot of innovation. Although some local AI apps on mobile devices have incorporated language translation capabilities, IBM is still a long way from reaching the stage when LLMs are profitable independent of cloud providers.

Smaller Language Models(SLMs) Smaller models, on the other hand, might revolutionise next-generation AI capabilities for mobile devices. Let’s look at these answers through the lens of a hybrid artificial intelligence paradigm.

The fundamentals of LLMs The unique class of AI models driving this new paradigm are called LLMs. NLP, or natural language processing, makes this possible. Developers employ vast amounts of data from multiple sources, including the internet, to train LLMs. Their size is a result of processing billions of parameters.

Although LLMs have extensive subject-matter knowledge, their application is restricted to the training set of data. This implies that they aren’t always “up to date” or precise. Large-scale machines (LLMs) are usually hosted on the cloud due to their size, necessitating the deployment of robust hardware with numerous GPUs.

This implies that businesses cannot employ LLMs out of the box when trying to mine information from their proprietary or confidential business data. They need to either build their own models or include their data with public LLMs in order to produce summaries, briefings, and answers to specific inquiries. Retrieval augmentation generation, or the RAG pattern, is a technique for adding one’s own data to an LLM. It is a general AI design pattern that enriches the LLM with outside data.

Small Language Models Is smaller better? Businesses in specialised industries, such as telecoms, healthcare, or oil and gas, are laser-focused. Smaller models would be more appropriate for them, even though normal gen AI scenarios and use cases can and can benefit them.

Personalized offers in service delivery, AI-powered chatbots for improved customer experience, and AI assistants in contact centres are a few common use cases in the telecom industry, to name a few. Enterprise data (rather than a public LLM) is most suited for use cases that assist telcos in enhancing network performance, boosting spectral efficiency in 5G networks, or pinpointing specific bottlenecks within their network.

This leads us to the conclusion that smaller is preferable. Small Language Models (SLMs) that are “smaller” than LLMs are now available. Whereas LLMs are taught on hundreds of billions of parameters, SLMs are trained on tens of billions. What’s more, SLMs are taught using data particular to a given domain. Despite having less contextual knowledge, individuals excel in the area they have chosen.

These models can be hosted in an enterprise’s data centre rather than the cloud because to their reduced size. SLMs may even be able to operate at scale on a single GPU chip, saving thousands of dollars in annual computational expenses. However, as chip design advances, it becomes more difficult to distinguish between applications that should only be operated in a corporate data centre or on the cloud.

Businesses may wish to run these SLMs in their data centres for a variety of reasons, including cost, data protection, and data sovereignty. The majority of businesses dislike having their data stored in the cloud. Performance is another important factor. Inferencing and computing are handled by Gen AI at the edge, which is faster and more secure than using a cloud provider because it happens as close to the data as feasible.

It is important to remember that SLMs are perfect for deployment on mobile devices and in contexts with limited resources because they demand less processing power.

An IBM Cloud Satellite site, which has a safe, fast connection to IBM Cloud housing the LLMs, could provide as an example of an on-premises location. Telcos could provide this option to their customers and host these SLMs at their base stations. All that has to be done is make the best use of GPUs, as this reduces the distance that data must travel and increases bandwidth.

To what extent can you shrink? Let us return to the initial inquiry on the compatibility of these models with mobile devices. The mobile gadget could be a car, a robot, or even an expensive phone. Manufacturers of devices have found that running LLMs requires a large amount of bandwidth. Smaller variants known as “tiny LLMs” can be used locally on medical equipment and smartphones.

Developers build these models using methods such as low-rank adaptation. They maintain a low number of trainable parameters while allowing users to fine-tune the models to specific requirements. Indeed, a TinyLlama project exists on GitHub.

Manufacturers of chips are working on creating chips that, through knowledge distillation and picture diffusion, can operate a condensed version of LLMs. Gen-AI tasks are carried out by edge devices with the help of neuro-processing units (NPUs) and system-on-chips (SOCs).

Solution architects ought to take into account the current state of technology, even when certain of these ideas are not yet operational. Working together and as SLMs and LLMs could be a good way to go. To offer a customised client experience, businesses can choose to develop their own AI models or employ the smaller, more niche models that are already available for their sector.

Hybrid AI Could hybrid AI be the solution? Though small LLMs on mobile edge devices are appealing and running SLMs on-premises looks feasible, what if the model needs a bigger corpus of data to react to certain prompts?

Cloud computing that is hybrid provides the best of both worlds. Could AI models be subject to the same rules? This idea is depicted in the graphic below.

The hybrid AI model may offer the opportunity to leverage LLM on the public cloud in situations where smaller models are inadequate. It is reasonable to allow this kind of technology. Businesses could use domain-specific SLMs to protect their data on-site, and when needed, they could access LLMs hosted in the public cloud. Spreading generative AI workloads this way appears to be a more effective manner as mobile devices with SOC become more competent.

The open source Mistral AI Model is now accessible on the Watson platform, according to a recent announcement from IBM. Comparing IBM’s small LLM to regular LLMs, it performs better and uses less resources while maintaining the same level of effectiveness. IBM also unveiled the Granite 7B model, which is a member of their reliable and well-curated family of foundation models.

IBM argue that instead of attempting to build their own generic LLMs, which they can readily access from multiple providers, enterprises should concentrate on building small, domain-specific models with internal enterprise data to differentiate their core competency and use insights from their data.

Not always is bigger better One company that might gain greatly from using this hybrid AI strategy is the telco industry. Because they may function as both providers and consumers, they play a special role. Healthcare, oil rigs, logistics firms, and other businesses may face similar situations. Are the telecom companies ready to leverage Gen AI effectively? Do they have a time-series model that matches the data, even though IBM know they have a tonne of it?

IBM has a multimodel approach for AI models in order to support every possible use case. Larger isn’t necessarily better because specialized models perform better than general-purpose models that require less infrastructure.

Read more on Govindhtech.com

#ibm #smallerlanguagemodel #slm #llm #ai #ibmcloud #technology #technews #govindhtech

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data-science-courses-in-india · 2 years ago

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M.Sc Data Science in India: A Promising Career Option

Data science is one of the most sought-after fields in the 21st century. It involves collecting, analysing, and interpreting large amounts of data to generate insights and solutions for various domains such as business, healthcare, education, and social sciences. Data science combines the skills of mathematics, statistics, computer science, and domain knowledge to create value from data.

If you are interested in pursuing a career in data science, you might be wondering what are the best options to study this field in India. In this article, we will explore the benefits of pursuing an M.Sc data science in India, the eligibility criteria, the curriculum, the career prospects, and some of the best MSc data science colleges in India.

Why Choose M.Sc Data Science in India?

You should consider pursuing an M.Sc data science in India for many reasons. Some of them are:

India is one of the fastest-growing economies in the world and has a massive demand for data scientists across various sectors. According to a report by NASSCOM, India will need 2.3 lakh data analytics professionals by 2023.

India has some of the best educational institutions that offer quality education and research opportunities in data science. You can learn from experienced faculty members, collaborate with peers and industry experts, and access state-of-the-art facilities and resources.

India offers a cost-effective option for studying data science compared to other countries. The average tuition fee for an MSc data science in India ranges from INR 50,000 to INR 3 lakhs per year, whereas the same degree in the US or UK can cost up to INR 20 lakhs per year.

India has a rich and diverse culture that can enrich your learning experience and personal growth. You can explore the history, heritage, cuisine, art, and music of different regions and communities in India.

What are the Eligibility Criteria for M.Sc Data Science in India?

To pursue an MSc data science in India, you must have a bachelor’s degree in any discipline with at least 50% marks from a recognised university. However, some colleges may prefer candidates with a background in mathematics, statistics, computer science, engineering, or related fields. You may also need to clear an entrance exam or an interview conducted by the respective college.

What is the Curriculum for M.Sc Data Science in India?

The curriculum for an MSc data science in India varies from college to college but generally covers the following topics:

Foundations of data science: This includes topics such as probability theory, linear algebra, calculus, discrete mathematics, optimisation techniques, etc.

Programming languages and tools: This includes topics such as Python, R, SQL, Excel, etc.

Data analysis and visualisation: This includes descriptive statistics, inferential statistics, hypothesis testing, regression analysis, clustering analysis, classification analysis, etc.

Machine learning and artificial intelligence: This includes supervised learning, unsupervised learning, reinforcement learning, neural networks, deep learning, natural language processing, computer vision, etc.

Big data and cloud computing: This includes topics such as Hadoop, Spark, MapReduce, NoSQL databases, AWS, Azure, etc.

Domain-specific applications: This includes topics such as business analytics, healthcare analytics, social media analytics, geospatial analytics, etc.

What are the Career Prospects for M.Sc Data Science in India?

After completing an MSc data science India, you can work as a data scientist, data analyst, data engineer, machine learning engineer, business analyst, AI engineer, or consultant in various sectors such as IT, banking, finance, retail, e-commerce, telecom, healthcare, education, government, etc.

You can also pursue higher studies such as PhD or post-doctoral research in data science or related fields.

According to Payscale.com, the average salary for a data scientist in India is INR 7.1 lakhs per year.

What are some of the Best MSc Data Science Colleges in India?

Many colleges offer an M.Sc data science in India, but some of the best ones are:

Indian Institute of Technology (IIT): IITs are among India's most prestigious institutes offering an MSc data science. The course lasts two years, and admission is based on GATE score and personal interview.

Indian Institute of Science (IISc): IISc is another renowned institute that offers an MSc data science in India. The course duration is two years, and the admission is based on the JAM score and personal interview.

Indian Statistical Institute (ISI): ISI is a premier institute that offers an MSc data science in India, focusing on statistics and mathematics. The course duration is two years, and the admission is based on an ISI admission test and personal interview.

Symbiosis Institute of Geoinformatics: This is one of the leading institutes offering an AI and data science course focusing on geospatial analytics. The course is two years, and the admission is based on SNAP test scores and personal interviews.

Chennai Mathematical Institute (CMI): CMI is a reputed institute offering an MSc data science in India focusing on mathematics and computer science. The course duration is two years, and the admission is based on a CMI entrance test and a personal interview.

Conclusion

Data science is a rewarding and exciting field that offers many opportunities for learning and growth. If you are interested in pursuing an MSc data science in India, you can choose from various colleges offering quality education and training. You can also explore the benefits of studying data science in India, such as the high demand, the low cost, and the rich culture. We hope this article has helped you understand the basics of MSc data science in India and inspired you to take the next step in your career.

#MScDataScience #DataScienceDegree #MasterInDataScience #DataScienceEducation #CareerInDataScience

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itsrahulpradeepposts · 5 years ago

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Which are the Top Global MNC recruiters looking to hire skilled Data Science freshers in Bangalore?

Data science- a word that has been creating buzz for quite sometime now. In recent years, the amount of data that has been produced by several industries is beyond calculation. Therefore, the concept of data science is what helps these companies handle this massive data. This is where the importance of data science lies. Data science can be thought of as a collection of disciplines and areas of expertise in order to get thorough, detailed and meaningful insights from raw data. Data science professionals must be skilled in everything starting from mathematics to statistics to computer programming to advanced visualisation to be able to distinguish the muddled masses from the important information and extract only the vital bits to make innovations happen. There has been a consistent growth in the demand for data scientists in India as various organisations are adopting new technologies and learning the meaningful insights of data through these technologies has become a necessity. Data science technology has turned out to be a very attractive career option for both freshers and experienced working professionals as the demand for data science jobs in sectors such as information technology, telecom, manufacturing, financial services, retail, automobiles, etc. is indeed very high. Therefore, it is very clear how data science as a career is very fruitful and if that is what you are looking for then down below is a detailed analysis of that.

Benefits of a data science career

A study conducted by a website recently revealed that there has been a notable increase in the data science and analytics domain with respect to job sectors in India in the recent years. This has led to the increase in the average salaries of these data science jobs and an increase in proportion of data science professionals, especially the experienced ones. When it comes to salary trends across various cities, Mumbai leads the chart of highest salaries with a whopping 15lakhs closely followed by Bangalore with a median salary of 14lakhs. The study also hinted that there has been a growth of about 14% in the salary range as compared to the previous year. So, it should be crystal clear by now that the data science career is of extreme value and pursuing data science technology as a career would really be beneficial. As companies across different sectors are using various data analysing tools to extract meaningful insights from raw data, it is most likely for the demand of data science jobs and professionals to increase in the near future. Data science technology is a new field and there is a lot of scope for this field to grow as the importance of data science will only increase rather than diminishing.

Sectors in India having an impact of data science

There is one area of data science which has been evolving rapidly and has left its impact in various areas. The subject or field is of Big data analytics. There are mainly two big avenues (largely) in India where big data analytics can have its reach because these are the two areas that have faced a major growth in big data: Commercial and Government. While the commercial part of India is largely focusing on the volume of big data or the aspect that is often related to when it comes to big data analytics, the government part of India on the other hand is focusing on the mining of unstructured data, the main constituent of big data which is for refining, for the fulfillment of data gaps. The real purpose of big data analytics is combining the structured and unstructured data and making the inference between them in real time. The firms that are currently present in India are yet to merge them in the most appropriate fashion. However, the efforts are going on in full length in order to make this happen. For a country like India, where the good and timely data are not much available in abundance in a panel structure, big data analytics have really proven and will continue to prove to be helpful. Big data analytics, for example, can be used for developing infrastructure, generation of employment, in tracking public service projects and other such stuff real quick.

Now, not just in the commercial sectors but there are also some other areas where data science is making revolutions silently and that is in the government areas. One of the major issues that is associated with the government is tracking of the projects in regular intervals for public health along with the monitoring of direct transfer of money as and when required to the poor people. This is where the importance of data science is as the government is putting in efforts to deal with these burning issues. Big data analytics can be used for checking the progress in the projects designed for the welfare of the society by analysing the satellite picture obtained to find out the truth. Also, tracking of the mobile phones and mapping the usage of them across the different districts will make it easier for figuring out the details of the benefit of the transfer. This mobile phone usage tracking is useful because this data is unstructured and therefore of major use for the government projects. Now, we shed some light on the commercial part of India. The data science applications are mainly found in banking sectors, finance departments and also data science in e-commerce has also shown a major progress. These sectors have realised the importance of data science as it is the only way to handle the humongous amount of data. The big companies such as Flipkart, Amazon have acknowledged the importance of data science in e-commerce and have utilised this technology. In this way, data science technology is emerging as the most sought after technology.

Making revolutions in Bangalore

Slowly and steadily, Bangalore has shown its growth when it comes to technologies and the latest trends related to it and data science is no exception. This city has managed to become the IT centre first and then data science hub of India eventually. From the various startups to the already established data science companies opening their shops or having their outlets in Bangalore, the city has proved to be the home of various companies of IT industries. Given the fact that this whole thing of data science is relatively new and data science and analytics is at a very formative stage, startups are doing pretty great. Most of these startups are based on services so keeping that in mind, they are really doing a good job. However, if the companies are to be considered, they are a bit advanced and the lack of talent or expertise in this field is quite notable. There is definitely a lack of qualified people with appropriate skills in data science as the field covers a lot of areas. This is where the importance of a data science course comes in as to learn data science, you definitely need to do a data science course. There are many data science programs available in India and just enrolling yourself in one of them would be the first step towards the path to learn data science. Now, coming back, as IT firms were already present in Bangalore, this has definitely contributed to the influence of data science in the city. Also, the presence of different institutions that have worked as learning sources, has been responsible for this change because the fresh talent is what might have had an influence. One such institution is Great Learning. The data science courses in Great Learning have really gained popularity over time as the students who have been there have praised these data science courses a lot.

Top Companies hiring Data Science freshers in Bangalore

There are many organisations and companies that are coming up with data science programs and are giving the opportunities to freshers and experienced people by offering more data science jobs or positions. There are many job openings in Bangalore as well by top companies who are willing to recruit these data science professionals. Let us see some of them.

1. Senior Data Scientist for Volvo Group in Bangalore:

For this company, the data scientist responsibilities would include understanding of the business requirements, looking into the KPIs and would be required to convert them into a technical hypothesis in the form of a structured and logical pattern along with the identification of the solution. Working with stakeholders throughout their term of service in order to identify and figure out the suitable opportunities or methods by which the company data can be used for the betterment of the business will be one of the major data scientist responsibilities in this company.

2. Senior Data Scientist for Amazon in Bangalore:

Amazon is looking for a data scientist who will also be a Subject Matter Expert or SME. This is one of those data scientist responsibilities that require helping the customers with the design of machine learning solutions that will work for the profit of the AI stack of the company. The data scientist responsibilities will also include development of white papers, blogs, referenced implementations and making presentations to figure out the artificial intelligence patterns and designs and learn about the best practices.

3. Data scientist for Brontominds in Bangalore:

The aim of this company is to qualify as the leading company when it comes to data science technology or artificial intelligence or analytics or automation as a business partner or as an employer. When appointed as the data scientist, the candidate will have to design and interpret the previously mentioned unstructured big data sources and figure out how that can benefit the customers or in other words, generate the actionable insights and effective solutions for the enhancement of the products of the clients. In short, the data scientist responsibilities will mainly have to deal with understanding the needs of the customers and also figuring out the ways that will benefit both the customers and the company.

4. Data science specialist for TeamLease Digital in Bangalore:

TeamLease Digital is a company which is a part of the company TeamLease Services Limited. The goal of this company is to provide IT stalves in various industries across the globe. Data scientists here will have to deal with a large amount of data and should be also capable of generating numerous reports. Therefore, experience in writing of algorithms and automating manual tasks is of vital importance because that is often required in this company.

5. Machine learning and analytics professional for Careernet Consulting in Bangalore:

The hired individual or candidate for this company is asked to work for the clients associated with banking sectors. The candidate should be experienced in analysing the medium complex problems and should be efficient in converting them into analytical approaches.

Hence, these are the major recruiting companies that are hiring candidates for data science jobs in Bangalore.

Conclusion

So, we have seen how data science is making revolutions and how companies are investing in hiring for data science jobs. However, in order to make yourself suitable for these jobs, you need to definitely do a data science course as there is no better way to learn data science and be qualified for these jobs. Among the several data science programs that are offered by various institutions, the data science courses in Great Learning are really productive because these data science courses not only help you grow individually but their placement cells make sure that you get into one of these top companies. So, if your aim is to learn data science then Great Learning should be the destination.

As it is seen, innovations are must for any field and the companies are just following that path in order to survive in this changing world of technology industry. The government as well as the private sectors are both putting in efforts to come up with data science based companies and the academic institutions in order to make this generation aware of this field. There is definitely still potential for growth for data science technology but the future of data science definitely seems brilliant and magnificent.

#data science #datascientist #machine learning #data architect #Great Learning

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techbugs-digital-marketing · 5 years ago

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Top 7 In-Demand Technology Skills to Learn in 2020

Top 17 In-Demand Technology Skills to Learn in 2020

If you graduated with a technology-related bachelor’s degree in India, you'd most likely want to specialize in finding your niche in the job market. One possible focus is mobile applications, one of the fastest-growing tech sectors in India and around the world. Simplilearn’s Social, Mobile, Analytics, and Cloud Certification Training takes a deep dive into this exciting sector.

1-Digital Marketing Services

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The most widely used technology by almost all the esteemed companies across domains (Banking, Telecom, Life Sciences, Financial Services, Aviation, Academia, Retail & Consumer products) around the world is nothing but Java. And when it comes to what exactly you will have to learn, Java continues to be an unending ocean where you should focus on strengthening your core part of it as much as possible. Aspirants normally get carried away with the charm of JSP & Servlets, but that kills you like sweet poison in the long run. Here is what ‘all’ you ‘must’ cover while opting for a Java course: J2SE (Core Part) J2EE (JSP & Servlets + EJB) STRUTS (Framework for Java) SPRING and/or HIBERNATE (For more sophisticated & advanced applications) There are many other frameworks and supporting technologies for Java aspirants but the above ones are a must and most demanding ones in the Software/IT market. To learn Java, there is no eligibility as such, but if you’re targeting a decent job then you will need a Bachelor/Master’s degree in computer domain (BE/BTech/BCA/BSc /MCA/MSc ) along with it. So if a career in any of the above major domains interests you, and the zeal for non-stop coding pleases your soul, then look no further than learning Java. Priority List of IT course options: JAVA/J2EE & its Frameworks (Struts, Spring, Hibernate) CISCO Technologies SAS DBA (Oracle, DB2, MySql, SQL Server) System Administration (Red Hat, Solaris, UNIX, VMware) Cloud Computing Microsoft Technologies Mobile SDKs (Android, iPhone, Windows Phone) Animation & Graphics SQT (Software Quality Testing) Other language courses(PHP, Ruby/Perl/Python)

3.CISCO Technologies

If you are fascinated by various types of network wires connected to your computer, the plugs, and switches that hold the programming logic of those cables, and are curious about how data flows (routes)through those switches, CISCO technologies can be your choice of course. To start with CISCO courses while targeting a decent job, you need at least a senior secondary (10+2) or some diploma/graduation in computer sciences (or even a non-computer profile will do). CISCO technologies are among the few courses which are most popular among non-IT aspirants who are targeting an entry-level job (but not without certain drawbacks viz. below-average work profile). While opting for any network-based course including CISCO technologies, do thorough research on the infrastructure of practical labs of the institute, as the theories are of almost negligent significance in network-based courses. The five levels of certification from CISCO are Entry, Associate, Professional, Expert, and Architect along with targeted certifications for Specialist and Technician. These are available in seven different subject areas: Routing & Switching, Design, Network Security, Service Provider, Storage Networking, Voice and Wireless. Remember, if you’ve jumped into the foray of CISCO technologies then their certifications are a ‘must’ to drive your career forward. The better the level of degree (graduation recommended) attained combined with the various levels of certifications achieved, the better will be the salary digits in your bank account and the work profile will vary hugely.

4. SAS

Data Analytics is now a rapidly growing field and so this Business Intelligence domain has emerged as the most lucrative option among the current breed of IT graduates. SAS, originally Statistical Analysis System, is an integrated system of software products provided by SAS Institute, to perform data entry, retrieval, management, mining, report writing, and graphics. Some explicit benefits are as under: Widely used for business planning, forecasting, and decision support for its accurate results. Extensively used for operations research and project management The best tool for quality improvement and applications development Provides Data warehousing (extract, transform, and load) Additional benefits of platform independence and remote computing ability SAS business solutions assist in areas such as human resource management, financial management, business intelligence, customer relationship management and more Used in the analysis of results and report generation in clinical trials in the pharmaceutical industry SAS comprises multi-engine architecture for better data management and reporting. SAS training prepares students for rewarding and very well paying career as SAS analyst, programmer, developer or consultant. Anyone can learn this course and appear for the certification exams, but usually, the ones who hold valid graduation in Computers/IT, are preferred. The SAS Certified Professional Program was launched by SAS Institute, Inc. in 1999 to recognise users who can demonstrate an in-depth understanding of SAS software. The program consists of five certifications across different functional areas. Several SAS courses prepare users for the certification exams. To date many programmers have taken these courses, some experienced users just take the exams, and many other SAS savvy professionals are experienced but not SAS certified. As per some recent surveys, about 60,000 SAS Analysts and programmers will be required in the next couple of years. Moreover, SAS consultants are paid the big bucks when compared to other software technologies. 5. Machine Learning Certification Course Also ideal for those aspiring to work in AI, this program focuses on machine learning and how specific techniques are used throughout the AI world. 6. AWS Solutions Architect Certification Training Course Participants can get certified as Amazon Web Services, or AWS, architects through this course, which covers essential skills and programs like VPC, EC2, IAM, and EBS. 7. AWS SysOps Associate Certificate Training Enrollees can master AWS platform deployment, management, and operations in this program, which is also a prerequisite to the AWS Certified DevOps Engineer exam. 8. AWS Developer Associate Certification Training This course is ideal for those looking to take the next step in their AWS training. Students learn how to write code, implement and test applications, and use an array of other tools.

12 free online education sites for tech skills

Alison Codeacademy Coursera Dash General Assembly EdX Harvard Online Learning Khan Academy MicrosoftLearn MIT OpenCourseWare Skillshare Udacity Udemy Read the full article

#DemandTechnologySkills #DigitalMarketingServices

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tak4hir0 · 5 years ago

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Machine learning in finance may work magic, even though there is no magic behind it (well, maybe just a little bit). Still, the success of machine learning project depends more on building efficient infrastructure, collecting suitable datasets, and applying the right algorithms. Machine learning is making significant inroads in the financial services industry. Let’s see why financial companies should care, what solutions they can implement with AI and machine learning, and how exactly they can apply this technology. Definitions We can define machine learning (ML) as a subset of data science that uses statistical models to draw insights and make predictions. The chart below explains how AI, data science, and machine learning are related. For the sake of simplicity, we focus on machine learning in this post. The magic about machine learning solutions is that they learn from experience without being explicitly programmed. To put it simply, you need to select the models and feed them with data. The model then automatically adjusts its parameters to improve outcomes. Data scientists train machine learning models with existing datasets and then apply well-trained models to real-life situations. The model runs as a background process and provides results automatically based on how it was trained. Data scientists can retrain models as frequently as required to keep them up-to-date and effective. For instance, our client Mercanto retrains machine learning models every day. In general, the more data you feed, the more accurate are the results. Coincidentally, enormous datasets are very common in the financial services industry. There are petabytes of data on transactions, customers, bills, money transfers, and so on. That is a perfect fit for machine learning. As the technology evolves and the best algorithms are open-sourced, it’s hard to imagine the future of the financial services without machine learning. That said, most financial services companies are still not ready to extract the real value from this technology for the following reasons: Businesses often have completely unrealistic expectations towards machine learning and its value for their organizations. AI and machine learning R&D is costly. The shortage of DS/ML engineers is another major concern. The figure below illustrates an explosive growth of demand for AI and machine learning skills. Financial incumbents are not agile enough when it comes to updating data infrastructure. We will talk about overcoming these issues later in this post. First, let’s see why financial services companies cannot afford to ignore machine learning. Why consider machine learning in finance? Despite the challenges, many financial companies already take advantage of this technology. The figure below shows that financial services’ execs take machine learning very seriously, and they do it for a bunch of good reasons: Reduced operational costs thanks to process automation. Increased revenues thanks to better productivity and enhanced user experiences. Better compliance and reinforced security. There is a wide range of open-source machine learning algorithms and tools that fit greatly with financial data. Additionally, established financial services companies have substantial funds that they can afford to spend on state-of-the-art computing hardware. Tanks to the quantitative nature of the financial domain and large volumes of historical data, machine learning is poised to enhance many aspects of the financial ecosystem. That is why so many financial companies are investing heavily in machine learning R&D. As for the laggards, it can prove to be costly to neglect AI and ML. What are machine learning use cases in finance? Let’s take a look at some promising machine learning applications in finance. Process Automation Process automation is one of the most common applications of machine learning in finance. The technology allows to replace manual work, automate repetitive tasks, and increase productivity. As a result, machine learning enables companies to optimize costs, improve customer experiences, and scale up services. Here are automation use cases of machine learning in finance: Chatbots Call-center automation. Paperwork automation. Gamification of employee training, and more. Below are some examples of process automation in banking: JPMorgan Chase launched a Contract Intelligence (COiN) platform that leverages Natural Language Processing, one of the machine learning techniques. The solution processes legal documents and extracts essential data from them. Manual review of 12,000 annual commercial credit agreements would typically take up around 360,000 labor hours. Whereas, machine learning allows to review the same number of contracts in a just a few hours. BNY Mello integrated process automation into their banking ecosystem. This innovation is responsible for $300,000 in annual savings and has brought about a wide range of operational improvements. Wells Fargo uses an AI-driven chatbot through the Facebook Messenger platform to communicate with users and provide assistance with passwords and accounts. Privatbank is a Ukrainian bank that implemented chatbot assistants across its mobile and web platforms. Chatbots sped up the resolution of general customer queries and allowed to decrease the number of human assistants. Security Security threats in finance are increasing along with the growing number of transaction, users, and third-party integrations. And machine learning algorithms are excellent at detecting frauds. For instance, banks can use this technology to monitor thousands of transaction parameters for every account in real time. The algorithm examines each action a cardholder takes and assesses if an attempted activity is characteristic of that particular user. Such model spots fraudulent behavior with high precision. If the system identifies suspicious account behavior, it can request additional identification from the user to validate the transaction. Or even block the transaction altogether, if there is at least 95% probability of it being a fraud. Machine learning algorithms need just a few seconds (or even split seconds) to assess a transaction. The speed helps to prevent frauds in real time, not just spot them after the crime has already been committed. Financial monitoring is another security use case for machine learning in finance. Data scientists can train the system to detect a large number of micropayments and flag such money laundering techniques as smurfing. Machine learning algorithms can significantly enhance network security, too. Data scientists train a system to spot and isolate cyber threats, as machine learning is second to none in analyzing thousands of parameters and real-time. And chances are this technology will power the most advanced cybersecurity networks in the nearest future. Adyen, Payoneer, Paypal, Stripe, and Skrill are some notable fintech companies that invest heavily in security machine learning. Underwriting and credit scoring Machine learning algorithms fit perfectly with the underwriting tasks that are so common in finance and insurance. Data scientists train models on thousands of customer profiles with hundreds of data entries for each customer. A well-trained system can then perform the same underwriting and credit-scoring tasks in the real-life environments. Such scoring engines help human employees work much faster and more accurately. Banks and insurance companies have a large number of historical consumer data, so they can use these entries to train machine learning models. Alternatively, they can leverage datasets generated by large telecom or utility companies. For instance, BBVA Bancomer is collaborating with an alternative credit-scoring platform Destacame. The bank aims to increase credit access for customers with thin credit history in Latin America. Destacame accesses bill payment information from utility companies via open APIs. Using bill payment behavior, Destacame produces a credit score for a customer and sends the result to the bank. Algorithmic trading In algorithmic trading, machine learning helps to make better trading decisions. A mathematical model monitors the news and trade results in real-time and detects patterns that can force stock prices to go up or down. It can then act proactively to sell, hold, or buy stocks according to its predictions. Machine learning algorithms can analyze thousands of data sources simultaneously, something that human traders cannot possibly achieve. Machine learning algorithms help human traders squeeze a slim advantage over the market average. And, given the vast volumes of trading operations, that small advantage often translates into significant profits. Robo-advisory Robo-advisors are now commonplace in the financial domain. Currently, there are two major applications of machine learning in the advisory domain. Portfolio management is an online wealth management service that uses algorithms and statistics to allocate, manage and optimize clients’ assets. Users enter their present financial assets and goals, say, saving a million dollars by the age of 50. A robo-advisor then allocates the current assets across investment opportunities based on the risk preferences and the desired goals. Recommendation of financial products. Many online insurance services use robo-advisors to recommend personalized insurance plans to a particular user. Customers choose robo-advisors over personal financial advisors due to lower fees, as well as personalized and calibrated recommendations. How to make use of machine learning in finance? In spite of all the advantages of AI and machine learning, even companies with deep pockets often have a hard time extracting the real value from this technology. Financial services incumbents want to exploit the unique opportunities of machine learning but, realistically, they have a vague idea of how data science works, and how to use it. Time and again, they encounter similar challenges like the lack of business KPIs. This, in turn, results in unrealistic estimates and drains budgets. It is not enough to have a suitable software infrastructure in place (although that would be a good start). It takes a clear vision, solid technical talent, and determination to deliver a valuable machine learning development project. As soon as you have a good understanding of how this technology will help to achieve business objectives, proceed with idea validation. This is a task for data scientists. They investigate the idea and help you formulate viable KPIs and make realistic estimates. Note that you need to have all the data collected at this point. Otherwise, you would need a data engineer to collect and clean up this data. Depending on a particular use case and business conditions, financial companies can follow different paths to adopt machine learning. Let’s check them out. Forgo machine learning and focus on big data engineering instead Often, financial companies start their machine learning projects only to realize they just need proper data engineering. Max Nechepurenko, a senior data scientist at N-iX, comments: When developing a [data science] solution, I’d advise using the Occam’s razor principle, which means not overcomplicating. Most companies that aim for machine learning in fact need to focus on solid data engineering, applying statistics to the aggregated data, and visualization of that data. Merely applying statistical models to processed and well-structured data would be enough for a bank to isolate various bottlenecks and inefficiencies in its operations. What are the examples of such bottlenecks? That could be queues at a specific branch, repetitive tasks that can be eliminated, inefficient HR activities, flaws of the mobile banking app, and so on. What’s more, the biggest part of any data science project comes down to building an orchestrated ecosystem of platforms that collect siloed data from hundreds of sources like CRMs, reporting software, spreadsheets, and more. Before applying any algorithms, you need to have the data appropriately structured and cleaned up. Only then, you can further turn that data into insights. In fact, ETL (extracting, transforming, and loading) and further cleaning of the data account for around 80% of the machine learning project’s time. Use third-party machine-learning solutions Even if your company decides to utilize machine learning in its upcoming project, you do not necessarily need to develop new algorithms and models. Most machine learning projects deal with issues that have already been addressed. Tech giants like Google, Microsoft, Amazon, and IBM sell machine learning software as a service. These out-of-the-box solutions are already trained to solve various business tasks. If your project covers the same use cases, do you believe your team can outperform algorithms from these tech titans with colossal R&D centers? One good example is Google’s multiple plug-and-play recommendation solutions. That software applies to various domains, and it is only logical to check if they fit to your business case. A machine learning engineer can implement the system focusing on your specific data and business domain. The specialist needs to extract the data from different sources, transform it to fit for this particular system, receive the results, and visualize the findings. The trade-offs are lack of control over the third-party system and limited solution flexibility. Besides, machine learning algorithms don’t fit into every use case. Ihar Rubanau, a senior data scientist at N-iX comments: A universal machine learning algorithm does not exist, yet. Data scientists need to adjust and fine-tune algorithms before applying them to different business cases across different domains. So if an existing solution from Google solves a specific task in your particular domain, you should probably use it. If not, aim for custom development and integration Innovation and integration Developing a machine learning solution from scratch is one of the riskiest, most costly and time-consuming options. Still, this may be the only way to apply ML technology to some business cases. Machine learning research and development targets a unique need in a particular niche, and it calls for an in-depth investigation. If there are no ready-to-use solutions that were developed to solve those specific problems, third-party machine learning software is likely to produce inaccurate results. Still, you will probably need to rely heavily on the open source machine learning libraries from Google and the likes. Current machine learning projects are mostly about applying existing state-of-the-art libraries to a particular domain and use case. At N-iX, we have identified seven common traits of a successful enterprise R&D project in machine learning. Here they are: A clear objective. Before collecting the data, you need at least some general understanding of the results you want to achieve with AI and machine learning. At the early stages of the project, data scientists will help you turn that idea into actual KPIs. Robust architecture design of the machine learning solution. You need an experienced software architect to execute this task. Appropriate big data engineering ecosystem (based on Apache Hadoop or Spark) is a must-have. It allows to collect, integrate, store, and process huge amounts of data from numerous siloed data sources of the financial services companies. Big data architect and big data engineers are responsible for constructing the ecosystem. Running ETL procedures (extract, transform, and load) on the newly created ecosystem. A big data architect or a machine learning engineer perform this task. The final data preparation. Besides data transformation and technical clean-up, data scientists may need to refine the data further to make it suitable for a specific business case. Applying appropriate algorithms, creating models based on these algorithms, fine-tuning models, and retraining models with new data. Data scientists and machine learning engineers perform these tasks. Lucid visualization of the insights. Business intelligence specialists are responsible for that. Besides, you may need frontend developers to create dashboards with easy-to-use UI. Small projects may require significantly less effort and a much smaller team. For instance, some R&D projects deal with small datasets, so they probably don’t need sophisticated big data engineering. In other instances, there is no need in complex dashboards or any data visualization at all. Key takeaways Financial incumbents most frequently use machine learning for process automation and security. Before collecting the data, you need to have a clear view of the results you expect from data science. There is a need to set viable KPIs and make realistic estimates before the project’s start. Many financial services companies need data engineering, statistics, and data visualization over data science and machine learning. The bigger and cleaner a training dataset is, the more accurate results a machine learning solution produces. You can retrain your models as frequently as you need without stopping machine learning algorithms. There is no universal machine learning solution to apply to different business cases. The machine learning development is costly. Tech giants like Google create machine learning solutions. If your project concerns such use cases, you cannot expect to outperform algorithms from Google, Amazon, or IBM. This article was originally published at www.n-ix.com

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analyticsindiam · 5 years ago

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Intel Readies For An AI Revolution With A Comprehensive AI Solutions Stack

Global technology player Intel has been a catalyst for some of the most significant technology transformations in the last 50 years, preparing its partners, customers and enterprise users for a digital era. In the area of artificial intelligence (AI) and deep learning (DL), Intel is at the forefront of providing end-to-end solutions that are creating immense business value. But there’s one more area where the technology giant is playing a central role. Intel is going to the heart of the developer community by providing a wealth of software and developer tools that can simplify building and deployment of DL-driven solutions and take care of all computing requirements so that data scientists, machine learning engineers and practitioners can focus on delivering solutions that grant real business value. The company’s software offerings provide a range of options to meet the varying needs of data scientists, developers and researchers at various levels of AI expertise. So, why are AI software development tools more important now than ever? As architectural diversity increases and the compute environment becomes more sophisticated, the developer community needs access to a comprehensive suite of tools that can enable them to build applications better, faster and more easily and reliably without worrying about the underlying architecture. What Intel is primarily doing is empowering coders, data scientists and researchers to become more productive by taking away the code complexity. Intel Makes AI More Accessible For The Developer Community In more ways than one, software has become the last mile between the developers and the underlying hardware infrastructure, enabling them to utilise the optimization capabilities of processors. Analytics India Magazine spoke to Akanksha Bilani, Country Lead – India, Singapore, ANZ at Intel Software to understand why, in today’s world, transformation of software is key to driving effective business, usage models and market opportunity. “Gone are the days where adding more racks to existing platforms helped drive productivity. Moore’s law and AI advocates that the way to take advantage of hardware is by driving innovation on software that runs on top of it. Studies show that modernization, parallelisation and optimization of software on the hardware helps in doubling the performance of our hardware,” she emphasizes. Going forward, the convergence of architecture innovation and optimized software for platforms will be the only way to harness the potential of future paradigms of AI, High Performance Computing (HPC) and the Internet of Everything (IoE). Intel’s Naveen Rao, Corporate Vice President and General Manager, Artificial Intelligence Products Group at Intel Corporation, summed up the above statement at the recently concluded AI Hardware1 summit. It’s not just a ‘fast chip’ - but a portfolio of products with a software roadmap that can enable the developer community to leverage the capabilities of the new AI hardware. “AI models are growing by 2x every 3 months. So it will take a village of technologies to meet the demands: 2x by software, 2x by architecture, 2x by silicon process and 4x by interconnect,” he stated. Simplifying AI Workflows With Intel® Software Development Tools As the global technology major leads the way forward in data-driven transformation, we are seeing Intel® Software2 solutions open up a new set of possibilities across multiple sectors. In retail, the Intel® Distribution of OpenVINO™ Toolkit is helping business leaders3 take advantage of near real-time insights to help make better decisions faster. Wipro4 has built groundbreaking edge AI solutions on server class Intel® Xeon® Scalable Processors and the Intel® Distribution of OpenVINO™ Toolkit. Today, data scientists who are building cutting-edge AI algorithms rely very heavily on Intel® Distribution for Python to get higher performance gains. While stock Python products bring a great deal performance to the table, the Intel performance libraries that come already plugged in with Intel® Distribution for Python help programmes gain more significant speed-ups as compared to the open source scikit-learn. Now, those working in distributed environments leverage BigDL, a DL library for Apache Spark. This distributed DL library helps data scientists accelerate DL inference on CPUs in their Spark environment. “BigDL is an add-on to the machine learning pipeline and delivers an incredible amount of performance gains,” Bilani elaborates. Then there’s also Intel® Data Analytics Acceleration Library (Intel® DAAL), widely used by data scientists for its range of algorithms, ranging from the most basic descriptive statistics for datasets to more advanced data mining and machine learning algorithms. For every stage in the development pipeline, there are tools providing APIs and it can be used with other popular data platforms such as Hadoop, Matlab, Spark and R. There is also another audience that Intel caters to — the tuning experts who really understand their programs and want to get the maximum performance out of their architecture. For these users, the company offers its Intel Math Kernel Library for Deep Neural Networks (Intel MKL-DNN) — an open source, performance-enhancing library which has been abstracted to a great extent to allow developers to utilise DL frameworks featuring optimized performance on Intel hardware. This platform can accelerate DL frameworks on Intel architecture and developers can also learn more about this tool through tutorials. The developer community is also excited about yet another ambitious undertaking from Intel, which will soon be out in beta and that truly takes away the complexity brought on by heterogeneous architectures. OneAPI, one of the most ground-breaking multi-year software projects from Intel, offers a single programming methodology across heterogeneous architectures. The end benefit to application developers is that they need no longer maintain separate code bases, multiple programming languages, and different tools and workflows which means that they can now get maximum performance out of their hardware. As Prakash Mallya, Vice President and Managing Director, Sales and Marketing Group, Intel India, explains, “The magic of OneAPI is that it takes away the complexity of the programme and developers can take advantage of the heterogeneity of architectures which implies they can use the architecture that best fits their usage model or use case. It is an ambitious multi-year project and we are committed to working through it every single day to ensure we simplify and not compromise our performance.” According to Bilani, the bottomline of leveraging OneAPI is that it provides an abstracted, unified programming language that actually delivers a one view/OneAPI across all the various architectures. OneAPI will be out in beta in October. How Intel Is Reimagining Computing As architectures get more diverse, Intel is doubling down on a broader roadmap for domain-specific architectures coupled with simplified software tools (libraries and frameworks) that enable abstraction and faster prototyping across its comprehensive AI solutions stack. The company is also scaling adoption of its hardware assets — CPUs, FPGAs, VPUs and the soon to be released Intel Nervana™ Neural Network Processor product line. As Mallya puts it, “Hardware is foundational to our company. We have been building architectures for the last 50 years and we are committed to doing that in the future but if there is one thing I would like to reinforce, it is that in an AI-driven world, as data-centric workloads become more diverse, there’s no single architecture that can fit in.” That’s why Intel focuses on multiple architectures — whether it is scalar (CPU), vector (GPU), matrix (AI) or spatial (FPGA). The Intel team is working towards offering more synchrony between all the hardware layers and software. For example, Intel Xeon Scalable processors have undergone generational improvements and are now seeing a drift towards instructions which are very specific to AI. Vector Neural Network Instruction (VNNI), built into the 2nd Generation Intel Xeon Scalable processors, delivers enhanced AI performance. Advanced Vector Extensions (AVX), on the other hand, are instructions that have already been a part of Intel Xeon technology for the last five years. While AVX allows engineers to get the performance they need on a Xeon processor, VNNI enables data scientists and machine learning engineers to maximize AI performance. Here’s where Intel is upping the game in terms of heterogeneity — from generic CPUs (2nd Gen Intel Xeon Scalable processors) running specific instructions for AI to actually having a complete product built for both training and inference. Earlier in August at the Hot Chips 2019, Intel announced the Intel Nervana Neural Network processors4, designed from the ground up to run full AI workloads that cannot run on GPUs which are more general purpose. The Bottomline: a) Deploy AI anywhere with unprecedented hardware choice b) Software capabilities that sit on top of hardware c) Enriching community support to get up to speed with the latest tools Winning the AI Race For Intel, the winning factor has been staying closely aligned with its strategy of ‘no one size fits all’ approach and ensuring its evolving portfolio of solutions and products stays AI-relevant. The technology behemoth has been at the forefront of the AI revolution, helping enterprises and startups operationalize AI by reimagining computing and offering full-stack AI solutions, spanning software and hardware that add additional value to end customers. Intel has also heavily built up a complete ecosystem of partnerships and has made significant inroads into specific industry verticals and applications like telecom, healthcare and retail, helping the company drive long-term growth. As Mallya sums up, the way forward is through meaningful collaborations and making the vision of AI for India a reality using powerful best-in-class tools. Sources 1AI Hardware Summit: https://twitter.com/karlfreund 2Intel Software Solutions: https://software.intel.com/en-us 3Accelerate Vision Anywhere With OpenVINO™ Toolkit: https://www.intel.in/content/www/in/en/internet-of-things/openvino-toolkit.html 4At Hot Chips, Intel Pushes ‘AI Everywhere’: https://newsroom.intel.com/news/hot-chips-2019/#gs.8w7pme Read the full article

#intel #intelai #intelartificialintelligence

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