#develop machine learning model from scratch

[education43]

What Are the Qualifications for a Data Scientist?

In today's data-driven world, the role of a data scientist has become one of the most coveted career paths. With businesses relying on data for decision-making, understanding customer behavior, and improving products, the demand for skilled professionals who can analyze, interpret, and extract value from data is at an all-time high. If you're wondering what qualifications are needed to become a successful data scientist, how DataCouncil can help you get there, and why a data science course in Pune is a great option, this blog has the answers.

The Key Qualifications for a Data Scientist

To succeed as a data scientist, a mix of technical skills, education, and hands-on experience is essential. Here are the core qualifications required:

1. Educational Background

A strong foundation in mathematics, statistics, or computer science is typically expected. Most data scientists hold at least a bachelor’s degree in one of these fields, with many pursuing higher education such as a master's or a Ph.D. A data science course in Pune with DataCouncil can bridge this gap, offering the academic and practical knowledge required for a strong start in the industry.

2. Proficiency in Programming Languages

Programming is at the heart of data science. You need to be comfortable with languages like Python, R, and SQL, which are widely used for data analysis, machine learning, and database management. A comprehensive data science course in Pune will teach these programming skills from scratch, ensuring you become proficient in coding for data science tasks.

3. Understanding of Machine Learning

Data scientists must have a solid grasp of machine learning techniques and algorithms such as regression, clustering, and decision trees. By enrolling in a DataCouncil course, you'll learn how to implement machine learning models to analyze data and make predictions, an essential qualification for landing a data science job.

4. Data Wrangling Skills

Raw data is often messy and unstructured, and a good data scientist needs to be adept at cleaning and processing data before it can be analyzed. DataCouncil's data science course in Pune includes practical training in tools like Pandas and Numpy for effective data wrangling, helping you develop a strong skill set in this critical area.

5. Statistical Knowledge

Statistical analysis forms the backbone of data science. Knowledge of probability, hypothesis testing, and statistical modeling allows data scientists to draw meaningful insights from data. A structured data science course in Pune offers the theoretical and practical aspects of statistics required to excel.

6. Communication and Data Visualization Skills

Being able to explain your findings in a clear and concise manner is crucial. Data scientists often need to communicate with non-technical stakeholders, making tools like Tableau, Power BI, and Matplotlib essential for creating insightful visualizations. DataCouncil’s data science course in Pune includes modules on data visualization, which can help you present data in a way that’s easy to understand.

7. Domain Knowledge

Apart from technical skills, understanding the industry you work in is a major asset. Whether it’s healthcare, finance, or e-commerce, knowing how data applies within your industry will set you apart from the competition. DataCouncil's data science course in Pune is designed to offer case studies from multiple industries, helping students gain domain-specific insights.

Why Choose DataCouncil for a Data Science Course in Pune?

If you're looking to build a successful career as a data scientist, enrolling in a data science course in Pune with DataCouncil can be your first step toward reaching your goals. Here’s why DataCouncil is the ideal choice:

Comprehensive Curriculum: The course covers everything from the basics of data science to advanced machine learning techniques.

Hands-On Projects: You'll work on real-world projects that mimic the challenges faced by data scientists in various industries.

Experienced Faculty: Learn from industry professionals who have years of experience in data science and analytics.

100% Placement Support: DataCouncil provides job assistance to help you land a data science job in Pune or anywhere else, making it a great investment in your future.

Flexible Learning Options: With both weekday and weekend batches, DataCouncil ensures that you can learn at your own pace without compromising your current commitments.

Conclusion

Becoming a data scientist requires a combination of technical expertise, analytical skills, and industry knowledge. By enrolling in a data science course in Pune with DataCouncil, you can gain all the qualifications you need to thrive in this exciting field. Whether you're a fresher looking to start your career or a professional wanting to upskill, this course will equip you with the knowledge, skills, and practical experience to succeed as a data scientist.

Explore DataCouncil’s offerings today and take the first step toward unlocking a rewarding career in data science! Looking for the best data science course in Pune? DataCouncil offers comprehensive data science classes in Pune, designed to equip you with the skills to excel in this booming field. Our data science course in Pune covers everything from data analysis to machine learning, with competitive data science course fees in Pune. We provide job-oriented programs, making us the best institute for data science in Pune with placement support. Explore online data science training in Pune and take your career to new heights!

[jcmarchi]

Entrepreneur creates career pathways with MIT OpenCourseWare

New Post has been published on https://thedigitalinsider.com/entrepreneur-creates-career-pathways-with-mit-opencourseware/

Entrepreneur creates career pathways with MIT OpenCourseWare

When June Odongo interviewed early-career electrical engineer Cynthia Wacheke for a software engineering position at her company, Wacheke lacked knowledge of computer science theory but showed potential in complex problem-solving.

Determined to give Wacheke a shot, Odongo turned to MIT OpenCourseWare to create a six-month “bridging course” modeled after the classes she once took as a computer science student. Part of MIT Open Learning, OpenCourseWare offers free, online, open educational resources from more than 2,500 courses that span the MIT undergraduate and graduate curriculum. 

“Wacheke had the potential and interest to do the work that needed to be done, so the way to solve this was for me to literally create a path for her to get that work done,” says Odongo, founder and CEO of Senga Technologies. 

Developers, Odongo says, are not easy to find. The OpenCourseWare educational resources provided a way to close that gap. “We put Wacheke through the course last year, and she is so impressive,” Odongo says. “Right now, she is doing our first machine learning models. It’s insane how good of a team member she is. She has done so much in such a short time.”

Making high-quality candidates job-ready

Wacheke, who holds a bachelor’s degree in electrical engineering from the University of Nairobi, started her professional career as a hardware engineer. She discovered a passion for software while working on a dashboard design project, and decided to pivot from hardware to software engineering. That’s when she discovered Senga Technologies, a logistics software and services company in Kenya catering to businesses that ship in Africa. 

Odongo founded Senga with the goal of simplifying and easing the supply chain and logistics experience, from the movement of goods to software tools. Senga’s ultimate goal, Odongo says, is to have most of their services driven by software. That means employees — and candidates — need to be able to think through complex problems using computer science theory.

“A lot of people are focused on programming, but we care less about programming and more about problem-solving,” says Odongo, who received a bachelor’s degree in computer science from the University of Massachusetts at Lowell and an MBA from Harvard Business School. “We actually apply the things people learn in computer science programs.”

Wacheke started the bridging course in June 2022 and was given six months to complete the curriculum on the MIT OpenCourseWare website. She took nine courses, including: Introduction to Algorithms; Mathematics for Computer Science; Design and Analysis of Algorithms; Elements of Software Construction; Automata, Computability, and Complexity; Database Systems; Principles of Autonomy and Decision Making; Introduction to Machine Learning; and Networks. 

“The bridging course helped me learn how to think through things,” Wacheke says. “It’s one thing to know how to do something, but it’s another to design that thing from scratch and implement it.”

During the bridging course, Wacheke was paired with a software engineer at Senga, who mentored her and answered questions along the way. She learned Ruby on Rails, a server-side web application framework under the MIT License. Wacheke also completed other projects to complement the theory she was learning. She created a new website that included an integration to channel external requests to Slack, a cross-platform team communication tool used by the company’s employees.

Continuous learning for team members

The bridging course concluded with a presentation to Senga employees, during which Wacheke explained how the company could use graph theory for decision-making. “If you want to get from point A to B, there are algorithms you can use to find the shortest path,” Wacheke says. “Since we’re a logistics company, I thought we could use this when we’re deciding which routes our trucks take.”

The presentation, which is the final requirement for the bridging course, is also a professional development opportunity for Senga employees. “This process is helpful for our team members, particularly those who have been out of school for a while,” Odongo says. “The candidates present what they’ve learned in relation to Senga. It’s a way of doing continuous learning for the existing team members.”

After successfully completing the bridging course in November 2022, Wacheke transitioned to a full-time software engineer role. She is currently developing a “machine” that can interpret and categorize hundreds of documents, including delivery notes, cash flows, and receipts.

“The goal is to enable our customers to simply feed those documents into our machine, and then we can more accurately read and convert them to digital formats to drive automation,” Odongo says. “The machine will also enable someone to ask a document a question, such as ‘What did I deliver to retailer X on date Y?’ or ‘What is the total price of the goods delivered?’”

The bridging course, which was initially custom-designed for Wacheke, is now a permanent program at Senga. A second team member completed the course in October 2023 and has joined the software team full time. 

“Developers are not easy to find, and you also want high-quality developers,” Odongo says. “At least when we do this, we know that the person has gone through what we need.”

[pers-books]

ARTIFICIAL INTELLIGENCE

This new data poisoning tool lets artists fight back against generative AI

The tool, called Nightshade, messes up training data in ways that could cause serious damage to image-generating AI models. 

By Melissa Heikkiläarchive October 23, 2023

October 23, 2023

A new tool lets artists add invisible changes to the pixels in their art before they upload it online so that if it’s scraped into an AI training set, it can cause the resulting model to break in chaotic and unpredictable ways. 

The tool, called Nightshade, is intended as a way to fight back against AI companies that use artists’ work to train their models without the creator’s permission. Using it to “poison” this training data could damage future iterations of image-generating AI models, such as DALL-E, Midjourney, and Stable Diffusion, by rendering some of their outputs useless—dogs become cats, cars become cows, and so forth. MIT Technology Review got an exclusive preview of the research, which has been submitted for peer review at computer security conference Usenix.   

AI companies such as OpenAI, Meta, Google, and Stability AI are facing a slew of lawsuits from artists who claim that their copyrighted material and personal information was scraped without consent or compensation. Ben Zhao, a professor at the University of Chicago, who led the team that created Nightshade, says the hope is that it will help tip the power balance back from AI companies towards artists, by creating a powerful deterrent against disrespecting artists’ copyright and intellectual property. Meta, Google, Stability AI, and OpenAI did not respond to MIT Technology Review’s request for comment on how they might respond. 

Zhao’s team also developed Glaze, a tool that allows artists to “mask” their own personal style to prevent it from being scraped by AI companies. It works in a similar way to Nightshade: by changing the pixels of images in subtle ways that are invisible to the human eye but manipulate machine-learning models to interpret the image as something different from what it actually shows. 

The team intends to integrate Nightshade into Glaze, and artists can choose whether they want to use the data-poisoning tool or not. The team is also making Nightshade open source, which would allow others to tinker with it and make their own versions. The more people use it and make their own versions of it, the more powerful the tool becomes, Zhao says. The data sets for large AI models can consist of billions of images, so the more poisoned images can be scraped into the model, the more damage the technique will cause. 

A targeted attack

Nightshade exploits a security vulnerability in generative AI models, one arising from the fact that they are trained on vast amounts of data—in this case, images that have been hoovered from the internet. Nightshade messes with those images. 

Artists who want to upload their work online but don’t want their images to be scraped by AI companies can upload them to Glaze and choose to mask it with an art style different from theirs. They can then also opt to use Nightshade. Once AI developers scrape the internet to get more data to tweak an existing AI model or build a new one, these poisoned samples make their way into the model’s data set and cause it to malfunction. 

Poisoned data samples can manipulate models into learning, for example, that images of hats are cakes, and images of handbags are toasters. The poisoned data is very difficult to remove, as it requires tech companies to painstakingly find and delete each corrupted sample. 

The researchers tested the attack on Stable Diffusion’s latest models and on an AI model they trained themselves from scratch. When they fed Stable Diffusion just 50 poisoned images of dogs and then prompted it to create images of dogs itself, the output started looking weird—creatures with too many limbs and cartoonish faces. With 300 poisoned samples, an attacker can manipulate Stable Diffusion to generate images of dogs to look like cats. 

Generative AI models are excellent at making connections between words, which helps the poison spread. Nightshade infects not only the word “dog” but all similar concepts, such as “puppy,” “husky,” and “wolf.” The poison attack also works on tangentially related images. For example, if the model scraped a poisoned image for the prompt “fantasy art,” the prompts “dragon” and “a castle in The Lord of the Rings” would similarly be manipulated into something else. 

Zhao admits there is a risk that people might abuse the data poisoning technique for malicious uses. However, he says attackers would need thousands of poisoned samples to inflict real damage on larger, more powerful models, as they are trained on billions of data samples. 

“We don’t yet know of robust defenses against these attacks. We haven’t yet seen poisoning attacks on modern [machine learning] models in the wild, but it could be just a matter of time,” says Vitaly Shmatikov, a professor at Cornell University who studies AI model security and was not involved in the research. “The time to work on defenses is now,” Shmatikov adds.

Gautam Kamath, an assistant professor at the University of Waterloo who researches data privacy and robustness in AI models and wasn’t involved in the study, says the work is “fantastic.” 

The research shows that vulnerabilities “don’t magically go away for these new models, and in fact only become more serious,” Kamath says. “This is especially true as these models become more powerful and people place more trust in them, since the stakes only rise over time.” 

A powerful deterrent

Junfeng Yang, a computer science professor at Columbia University, who has studied the security of deep-learning systems and wasn’t involved in the work, says Nightshade could have a big impact if it makes AI companies respect artists’ rights more—for example, by being more willing to pay out royalties.

AI companies that have developed generative text-to-image models, such as Stability AI and OpenAI, have offered to let artists opt out of having their images used to train future versions of the models. But artists say this is not enough. Eva Toorenent, an illustrator and artist who has used Glaze, says opt-out policies require artists to jump through hoops and still leave tech companies with all the power. 

Toorenent hopes Nightshade will change the status quo. 

“It is going to make [AI companies] think twice, because they have the possibility of destroying their entire model by taking our work without our consent,” she says. 

Autumn Beverly, another artist, says tools like Nightshade and Glaze have given her the confidence to post her work online again. She previously removed it from the internet after discovering it had been scraped without her consent into the popular LAION image database. 

“I’m just really grateful that we have a tool that can help return the power back to the artists for their own work,” she says.

[1mpulsee]

bart has a major interest in engineering .

it took him a while to pick up any real hobbies, for a long time he tried a little of everything to feel out what kind of things he enjoyed . reading is a favorite of his, and that lead to him finding a lot of books on machinery . he ended up with an entire collection of engineering textbooks and books about all kinds of machines, finding it to be a topic he was extremely interested in .

soon he wanted to properly try it for himself, so he started taking apart and putting back together just about anything he could get his hands on . with his super speed, genius level intelligence, and perfect memory, it became clear that he had a lot of talent for the field . he could disassemble and reassemble any machine he came across in minutes, but that became boring . he wanted to make things from scratch, and he found that to be even more fun than just disassembly .

while working on various engineering projects that ranged from simple experiments to fully fleshed out inventions, he stumbled upon a subclass of engineering he had particular interest in — chronal tech, machinery surrounding time manipulation . his background in being caught in alternate timelines gave him a new insight on the science, and he found himself eager to find out everything there was available about it . when he found that most of chronal tech was theory and there were little to no actual real life applications, that became his new interest . he studied theories, came up with his own, developed blueprints, and even made a few models and prototypes . the complicated nature of chronal technology only strengthened his skill in engineering, and he now has a more than advanced understanding of it .

he is extremely knowledgeable on the subject, and is capable of applying that knowledge just as well as he can learn it . machinery is one of the few passions and hobbies he has stuck with for a while that didn’t have to do with being a superhero . however, he often doesn’t disclose this talent or try to bring attention to it out of worry that it won’t be taken seriously or seen as strange . most of his inventions, machines, and blueprints are kept out of easy access for anyone except his close friends and family . he won’t outright deny it, but he doesn’t tend to bring it up .

[iplayedgame]

As I did my small research on this topic and a year ago in reddit there were some conversations regarding to this. So overall conclusion before I dive into this topic - AI art is a slippery slope to walk with. 

1)  As some users stated in reddit there are no proper rules in USA and Europe, so basically if You use AI-generated banner/cover/main character etc., You might not be able to stop anyone from copying it and using it for themselves. But as some users stated, worries are not so much about individual pictures, but it could be possible to rip off whole game. 

2) Machine learning models themselves, it is known that they are trained on copyrighted materials, some of those materials are meant to never be publicly available on the internet. Multiple companies and artists are filing lawsuits against them. So in future it will boil down if it falls into derivative or transformative use.

3) Assuming AI content falls in transformative case in future - if AI used is not owned by You, then these assets are not Yours and You can’t copyright them, so even You made Your work on these assets it is not sufficient enough for the courts.

4) Logically thinking if AI generated picture is modified enough it should be able to be copyrighted, but how much modification is needed is hard to say. As some users stated they use this approach and not tell anyone that they used AI.

5) Some users stated that AI can be safe to say to be a tool to use with Your own images to assist You in asset creation rather than creating them from scratch.

Platforms like Steam seem to stay on safe side and they reject those games, quote from Valve: “As the legal ownership of such AI-generated art is unclear, we cannot ship your game while it contains these AI-generated assets, unless you can affirmatively confirm that you own the rights to all of the IP used in the data set that trained the AI to create the assets in your game,”

Recently Valve corporation updated their rules which will define how game developers can publish that use AI technology on Steam. Rules for AI generated content in games are meant to increase transparency, so customers can make an informed choice to buy it or not. As said in Valve’s post: “making changes to how we handle games that use AI technology will enable us to release the vast majority of games that use”

Under new rules developers will need to disclose when games contain AI generated content & confirm that it’s not illegal or infringing. Also need to say if a game has AI content generated “live” while the game is running, cause then there should be safety mechanisms to be able to stop their AI generating illegal content in future.

So these rules are changing fast and no clear vision is seen, but as we know money at the end runs the legal system and it could turn out as at the end big corporations will benefit from it and hurt rest of using AI. As one user stated in reddit about possible future scenarios:

1)  Worst case - Any games, products using games that can’t pay  ‘AI tax bill’ become illegal in commercial sense.

2) Reality case  - Businesses that are investing in AI are leaving anyone behind, so as AI isn’t going away in next 2-5 years every creative production team use AI or are out of business.

3) Hope case - Human generated creativity will retain it’s true value, maybe increase it. Example: will need video proof of creation process as part of validating / authenticating your product. 

[loveloki555]

Academy Maios

Maios means white stone. It's name from mines of white stones called vil. These are stones used in technology, medicine, transport… They are very valuable mines. Thanks to the successes, investments were made in a modern school facility. The school is located on a sea platform. The studies last 7 years. From the age of 7 to 14. Students live in dormitories. An unusual teaching method is group teaching. There are no large classes. Students form groups of four. Entry into the group is selected by combining students who have the most similar schedule - in the Academy you choose 7 subjects to study out of 42. Groups of four always occupy their own table and receive tasks from the teacher or AI assistant.

42 classes to choose from

Alchemy - the study of magic to create gold and other raw materials.

Herbalism - creating herbariums, classes in the orangery with magical plants, making herbal ointments.

Feasts - learning how to organize feasts, important for future diplomats, rulers and estate owners.

Fashion - learning fashion design and using machines to quickly create a given model of clothes.

Books - learning about being a collector of books, creating them in ancient bindings and properly sorting them.

Potions - classes on creating potions, medicines and magical drinks.

AI - the science of creating and connecting with AI.

Hotel - learning about running a hotel and solutions on how to deal with guests from a thousand worlds.

Divination - activities for people with the gift to develop their natural vision abilities.

Swimming - classes for swimmers, swimming is the main sport of the school apart from fencing and fan dancing.

Fencing - classes on all fencing techniques from thousands of worlds.

Dancing with fans - classes teaching airy dancing with two large fans. Dancers usually dance at every school function.

Fauna - the study of caring for animals, students spend most of their time in the aviary and butterfly house, which are the pride of the school.

Literature - a cross-section of literature from a thousand worlds and their interpretation.

Cello - classes for future cellists, the cello is the main instrument of the school.

Flying - learning to fly on a broom, carpet, trunk, and finally in person without any aids.

Politics - the science of governance, systems, laws of power and manipulation.

History - a cross-section of history from a thousand worlds, learning about similarities and differences in cultures.

Sand magic - magic based on shaping sand.

Water magic - magic based on shaping water.

Etiquette and good manners - a review of good manners from a thousand worlds.

Cooking - learning to cook from scratch to master level.

Technoparks - science about creating and managing a technopark.

Astronomy - classes for future astronauts who want to study the stars or be pilots on ships.

Chemistry - the scientific study of chemistry and its properties.

Meditation - learning how to meditate and control your focus, thoughts and breathing.

Dance - classes in dances from different worlds.

Skating - a sport, for ice skaters. It is not one of the school's main sports, but it is respected.

Engineering and programming - for future programmers and code creators.

Art - a review of art from a thousand worlds.

Planets - the study of the cultures and resources of a thousand planets.

Law - an overview of legal systems from a thousand worlds, legal theory.

Writing - classes for aspiring writers.

Philosophy - a review of philosophical works from a thousand worlds.

Theater - staging plays, learning about great works of theater.

Mnemonics - memorization activities, gathering information and knowledge.

Mathematics - an exact science for engineers, scientists and all representatives of science.

Languages - classes for polyglots, learning language mechanisms, the standard is learning 10 selected languages.

Physics and magic - classes about how physics and magic work, how they work and how they can be used.

Vil - learning about vil mines and mines for future engineers.

Fariza - learning to play the fariza instrument - called the ladies' instrument.

Jewelry - theory and creation of jewelry and jewelry wonders.

[yourglobalexpansionpartner]

Why Outsourcing in India Is the Backbone of Global Startups

In today's fast-paced business world, startups are constantly looking for ways to optimize their operations and scale rapidly while minimizing costs. One of the most effective strategies that have emerged is outsourcing key functions to countries that offer cost-effective and high-quality services. Outsourcing in India has become the backbone of global startups due to the country's skilled workforce, cost advantages, and ability to provide innovation-driven solutions. In this article, we will explore why outsourcing to India is a game-changer for startups around the world.

1. Cost-Effective Solutions

Cost efficiency is one of the primary reasons startups choose outsourcing in India. India offers a highly competitive advantage in terms of operational costs. By outsourcing critical functions like software development, customer support, data analysis, and marketing services, startups can significantly reduce their overheads. This cost-saving approach is crucial for startups that need to optimize their budgets without compromising on quality.

2. Access to Skilled Talent

India is home to one of the largest pools of talented professionals globally. With a vast number of highly qualified engineers, IT specialists, designers, marketers, and content creators, India offers a talent-rich environment for global startups. Outsourcing in India allows startups to tap into this reservoir of skilled human capital without the need for extensive training or hiring processes. The availability of a highly educated, English-speaking workforce makes it easy for startups to find the right talent for their needs.

3. Focus on Core Competencies

Outsourcing in India enables startups to focus on what they do best – developing innovative products, creating new services, and engaging with their customers. By outsourcing non-core activities such as customer service, administrative tasks, or IT support, startups can allocate more resources to their primary business objectives. This allows for enhanced productivity and growth, as founders and core teams can concentrate on their core competencies and strategic initiatives.

4. Scalability and Flexibility

One of the key challenges for startups is the ability to scale quickly in response to market demand. Outsourcing in India provides startups with the scalability and flexibility they need to expand without the burden of hiring large in-house teams. Whether it’s expanding customer support during peak periods, ramping up marketing efforts, or developing new software features, outsourcing partners in India can quickly adjust to changing requirements, helping startups stay agile and competitive.

5. Innovation and Technological Advancement

India has become a hub for innovation and technology, with an ever-growing number of tech startups, research initiatives, and advancements in artificial intelligence (AI), machine learning (ML), blockchain, and other emerging technologies. By outsourcing in India, startups gain access to cutting-edge solutions and expertise that can help them stay ahead of the curve. This partnership can foster innovation, allowing startups to leverage the latest technologies without having to build them from scratch.

6. 24/7 Operations and Global Reach

India’s time zone advantage enables startups to provide 24/7 operations, which is especially crucial for companies targeting global markets. Whether it's customer support, monitoring, or back-office operations, outsourcing in India allows startups to offer round-the-clock services to their clients. This continuous workflow ensures faster response times, improved customer satisfaction, and greater market penetration.

7. Risk Mitigation and Business Continuity

Startups are often vulnerable to risks that can disrupt operations, such as sudden workforce shortages or technical issues. By outsourcing to India, startups can mitigate these risks through a diversified operational model. Having teams in different locations helps ensure business continuity even in the face of unexpected challenges. This enables startups to maintain steady progress and minimize the impact of unforeseen disruptions.

Conclusion: The Strategic Advantage of Outsourcing in India

Outsourcing in India has proven to be a strategic decision for startups looking to grow efficiently and cost-effectively while maintaining a high standard of quality. With a large pool of skilled professionals, access to the latest technologies, and the flexibility to scale operations, India has become the go-to destination for startups around the world. By partnering with outsourcing providers in India, startups can optimize their operations, reduce costs, and focus on innovation and growth.

At Fox&Angel, we understand the pivotal role outsourcing plays in scaling businesses globally. If you're a startup looking to expand your operations, improve efficiency, and gain access to world-class talent, contact us to explore how outsourcing to India can propel your business forward.

[spookywhisperomen]

Build Smarter with AI Development, Machine Learning, and Expert Engineers  

In the digital world, businesses must evolve faster, serve smarter, and innovate constantly. Artificial Intelligence (AI) is no longer optional; it’s the foundation of next-gen business models. Whether it's automating processes, unlocking customer insights, or enabling smarter decisions, AI development is powering the future of every industry.

This article explores how AI, through machine learning, AI as a Service, ChatGPT integration, and more, is helping organizations build smarter, scale faster, and gain a competitive advantage.

The Power of AI Development  :

AI development is more than just algorithms. It’s about building intelligent systems that can learn, evolve, and improve business processes. Whether you're a startup or a Fortune 500 corporation, these systems enable automated processes, predictive analytics, intelligent customer engagement, and data-based and data-driven decision-making. As more AI tools become available to businesses, they are investigating opportunities that fall under AI Co-Pilot development, AI Avatar development, and Enterprise Artificial Intelligence solutions to obtain a competitive advantage.

Why Machine Learning Development Matters  :

Machine Learning is the backbone of AI and is the discipline that teaches systems how to learn from data, make inferences, and identify patterns. From recommendation engines to fraud detection, machine learning is transforming how businesses address complex problems. In retail and e-commerce, ML is transformative. Machine Learning in retail helps to predict inventory needs, provide personalized marketing experiences, and set prices dynamically. Businesses can anticipate the needs of the consumer, sell the right products at the right time, and improve customer satisfaction while generating sales growth.

AI as a Service: Scalable and Flexible:  

Not every business has the resources to build AI systems from scratch. That’s where AI as a Service (AIaaS) comes in. AIaaS provides access to pre-built AI tools and platforms through cloud-based services. This allows companies to integrate AI functionalities—like natural language processing, computer vision, or chatbot automation—without needing in-house expertise.

This model offers scalability, flexibility, and reduced time to market. Businesses can experiment, iterate, and deploy AI solutions rapidly while keeping costs in check.

Hire AI Engineers to Power Your Vision:  

Behind every intelligent AI system is a team of skilled engineers. Whether you’re developing a custom chatbot, implementing ML models, or creating advanced virtual assistants, you need the right talent.

Hire AI engineers who not only understand the technical side of development but also grasp your business objectives. An AI talent that holds all the knowledge to create scalable solutions is secure and aligned with your business goals. From system architecture to model deployment, very experienced engineers get things done.

Enterprise AI Solutions: Transforming Operations  :

Large organizations are increasingly investing in Enterprise AI solutions to digitise operations and enhance productivity. These solutions include intelligent automation, customer behaviour analysis, and AI-powered business intelligence tools.

By deploying AI across departments—HR, finance, logistics, and customer service—enterprises can streamline workflows, cut costs, and uncover valuable insights. For example, AI can optimize supply chains, automate invoice processing, or forecast sales trends.

Enterprise AI isn't just about efficiency—it’s about innovation. Organizations using AI can adapt faster to market changes, introduce new services, and lead industry disruption.

The Rise of AI Co-Pilot Development  :

Imagine having an AI assistant embedded into your daily tools, guiding decisions, automating tasks, and enhancing productivity. That’s the idea behind AI Co-Pilot development.

AI co-pilots can integrate with CRMs, ERPs, or even email and chat platforms to provide contextual recommendations, generate content, and automate repetitive tasks. Think of them as intelligent sidekicks that amplify human potential.

From project management to customer service, co-pilots are reshaping the workplace, reducing manual workloads and improving accuracy.

Chatbots and Conversational AI: Hire ChatGPT Developers  :

As AI continues to evolve, conversational interfaces are becoming standard for customer engagement. Businesses are looking to hire ChatGPT developers to build smart chatbots capable of understanding and responding in natural language.

Powered by GPT models, these bots can answer complex questions, handle support tickets, guide users through sales funnels, and even generate human-like content. They offer 24/7 support, reduce response times, and increase customer satisfaction—all without hiring more staff.

Whether it’s a retail website, a banking app, or a healthcare platform, ChatGPT-powered bots can be customized to serve specific needs with remarkable accuracy and empathy.

The Future Is Personal: AI Avatar Development:  

Visual AI is opening exciting new possibilities—especially through AI Avatar development. These lifelike, animated digital personas can engage users in real-time via websites, apps, or VR environments. They combine speech, facial expressions, and gestures for an immersive experience.

Use cases range from virtual customer service reps to interactive brand ambassadors and e-learning guides. AI avatars can humanise digital interactions, build brand trust, and offer personalized service at scale.

As generative AI evolves, avatars are becoming more expressive, more responsive, and increasingly indistinguishable from real people, offering futuristic engagement today.

Build Smarter, Act Faster  :

The message is clear: to remain competitive, businesses need to build smarter with a strategic AI roadmap. Whether you're optimising internal operations, revolutionising customer experience, or launching new AI-driven products, success starts with a solid foundation in:

AI Development

Machine Learning Development

AI as a Service

Hiring top-tier AI Engineers

Implementing ML in Retail

Deploying Enterprise AI Solutions

Hiring ChatGPT Developers

Creating AI Co-Pilots

Developing AI Avatars

Investing in AI today is not just about technology—it's about vision. It’s about unlocking growth, agility, and intelligence across your organization.

Conclusion:  

AI is no longer just a buzzword—it’s a business imperative. From startups to global enterprises, forward-thinking companies are leveraging AI to gain real-time insights, automate decision-making, and create personalized experiences.

If you're ready to take the next step, hire AI experts, embrace cutting-edge AI services, and lead your industry with smart, scalable solutions. The future belongs to those who build smarter—and that future starts now.At Osiz Technologies, we specialise in AI Development and Machine Learning Development, offering businesses scalable AI as a Service solutions that drive growth and efficiency. Whether you're looking to hire AI engineers for custom projects or explore the power of Large Language Model Development and AI Co-Pilot Development, our team is here to bring your vision to life. We also provide industry-specific solutions, such as Machine Learning in Retail and Enterprise AI Solutions, to help businesses stay ahead of the competition. From ChatGPT Developers to AI Avatar Development, we deliver innovative tools that transform customer experiences and enhance decision-making. Trust Osiz Technologies to help you build smarter, faster, and more personalized AI-driven solutions for the future.

[apimarket]

Why Now is the Right Time to Buy API for Your Business

If you're building a product, app, or digital service, there’s one proven way to save time, effort, and cost - you buy API that already does the heavy lifting. APIs (Application Programming Interfaces) allow applications to talk to each other, unlocking features without the need to build everything from scratch. At API Market, we offer a wide collection of high-quality APIs across multiple categories such as AI, image processing, machine learning, and data analytics. Whether you're a startup founder, a freelance developer, or part of an enterprise tech team, you’ll find what you need to accelerate your project. APIs help you move faster. They let you focus on what matters most - your product’s core experience while still giving users top-tier functionality powered by experts in the field.

What Should You Know Before You Buy API?

Before you buy API, it’s worth reviewing a few key factors:

Reliability: Does the API have consistent uptime and quick response times?

Documentation: Is it beginner-friendly and clear enough to integrate smoothly?

Scalability: Can it support a growing user base without slowing down?

Security: Are best practices followed for authentication and data protection?

At API Market, each API listing is reviewed to make sure it meets professional standards. From pricing clarity to real-time performance stats and detailed documentation, everything is laid out so you can make confident decisions.

Why Developers Love to Buy API Through API Market

We’ve designed our platform to be developer-first. You can easily test an API before purchase, explore categories by use case, and manage everything from one clean dashboard. There’s no complex licensing, no tedious paperwork - just simple onboarding, secure access, and a flexible pricing model that supports your business at every stage. Whether you’re building an app, automating a workflow, or adding AI to a platform, API Market helps you go from idea to launch faster.

Final Thoughts - It’s Smarter to Buy API, Not Build Everything

In a world that values speed and efficiency, reinventing the wheel just doesn’t make sense. When you buy API from API Market, you're not only cutting down development time - you're investing in reliability, performance, and support that scales with you. Browse the APIs on API Market today and discover how easy it is to integrate powerful functionality into your product without the guesswork. Build smart, build fast and always choose to buy API the right way.

[abigailnussbaum]

I've been noticing this too, and it feels like the most insidious tool in the AI industry's efforts to convince us that their product - which is prohibitively expensive, legally dubious, and objectively does not work - is "inevitable". It probably helps that it really does seem to be an organic phenomenon - non-AI tech company CEOs, having been incepted into the idea that this technology is the future, are rushing to slap it on their products for fear of being left behind. So, inasmuch as "AI" as it's being used today has a concrete meaning - a large language model used for text or image generation in response to natural language queries - it's now being applied to any kind of algorithmic tool, whether preexisting or new, in the misguided belief that this makes it sound more sophisticated.

For an example that I've witnessed close-hand, my company's VP of R&D recently announced, in an all-hands state of the company talk, that we were looking into incorporating AI into our product. Which raised a lot of eyebrows among us engineers, since our products are high speed network routers that we sell to telecoms and other internet service providers. Not only do they have no use for text or image generation, you can't assume that they'd have access to a service like ChatGPT (just because a device passes network traffic doesn't mean it has access to the open internet; your home wifi router doesn't necessarily have the ability to surf the web). Not to mention that our customers might have a problem with us passing their customer data to a third-party server. And since these are all firmware-based devices, we don't have the spare memory or computing capabilities to run our own LLM engine on each device.

Turns out he was talking about some kind of fancy algorithm to evaluate network load and adjust capacity dynamically. Currently we offer fairly sophisticated tools that allow customers to designate specific bandwidth channels for each of their customers, and prioritize different types of traffic (for example, if you're a telecom, voice traffic is always prioritized over data traffic, because a dropped packet in voice registers in a way that a dropped data packet doesn't). But this all has to be configured ahead of time, and the idea with this new tool is to be able to analyze the traffic in real time and respond to it without operator input. It sounds really neat, and I wouldn't be surprised if there's a machine learning component to it, but it's not what AI as the term is currently being used actually means. It's honestly a bit sad to me that if we actually get this kind of genuinely exciting, innovative tool off the ground, we'll have to slap the name of an environmentally wasteful plagiarism engine on it to get customers interested.

(There's also been some talk about using AI in coding. Apparently there's a team working on tools for that, though they haven't presented them to us yet. Personally, I'm dubious. Most of my work involves plugging into preexisting code, some of it decades old. It requires understanding systems, and the coding part is actually the smallest aspect of it. And yes, when I've developed features from scratch, there's a degree of repetition that a text generator might be able to help with - but as it happens, I already have tools that do some of that work, generating, for example, the code for new user commands from a definition document. But those tools are called compilers - the actual definition of "compiler" is any program that translates from one machine language to another - and I don't see how an AI tool to do similar work would save me that much time.)

Latest tech pet peeve is the use of the term "AI" to refer to basically anything that does any amount of automation or uses computers in any way

[callofdutymobileindia]

Artificial Intelligence Course in Bengaluru: Everything You Need to Know in 2025

Artificial Intelligence (AI) is no longer a futuristic concept; it's a present-day necessity. From personalized recommendations on streaming platforms to self-driving vehicles, AI has seeped into every corner of our lives. As businesses across the globe ramp up their AI capabilities, the demand for skilled professionals is skyrocketing. And in India, Bengaluru—widely known as the Silicon Valley of the country—has become a thriving hub for AI education and innovation.

If you're considering a career in AI or aiming to upskill, enrolling in an Artificial Intelligence course in Bengaluru could be the smartest move of your professional journey. In this article, we’ll explore why Bengaluru is the ideal city for AI education, what top courses offer, key features to look for, career outcomes, and much more.

Why Choose for an Artificial Intelligence Course in Bengaluru?

1. Technology Ecosystem

Bengaluru is home to over 10,000 tech start-ups and major R&D centres for companies like Google, Amazon, Microsoft, and IBM. This vibrant tech ecosystem ensures that AI learners get the best of academic knowledge and real-world exposure.

2. Career Opportunities

With AI being implemented in industries such as healthcare, finance, logistics, and e-commerce, job opportunities in Bengaluru are abundant. The city offers roles ranging from AI Engineer, Machine Learning Developer, Data Scientist, to NLP Specialist and Computer Vision Expert.

3. Educational Infrastructure

Bengaluru houses top institutes and training centres that offer industry-relevant AI courses. These include both academic institutions and private learning centers that blend theoretical learning with practical projects.

What You Will Learn in an AI Course in Bengaluru?

A comprehensive Artificial Intelligence course in Bengaluru typically includes a wide range of modules tailored to help learners build strong foundational and advanced knowledge.

Core Topics Usually Covered:

Python Programming for AI

Mathematics for AI (Linear Algebra, Calculus, Probability)

Machine Learning Algorithms

Deep Learning (using TensorFlow, Keras, or PyTorch)

Natural Language Processing (NLP)

Computer Vision

Reinforcement Learning

Model Deployment and MLOps

Ethical AI and Responsible Development

Hands-on Learning:

Expect courses to provide hands-on projects in domains like healthcare diagnosis using image processing, AI-based chatbots, fraud detection systems, and more.

Who Can Enroll in an AI Course?

Whether you’re a fresh graduate, working professional, or career switcher, there's an AI course designed for your level. Most programs require basic knowledge of programming and statistics. However, some beginner-friendly courses also start from scratch.

Ideal Candidates:

Computer Science Graduates

Data Analysts/Engineers

IT Professionals

Engineers from any discipline

Business Professionals looking to transition into tech roles

Top Features to Look for in an AI Course in Bengaluru

Choosing the right Artificial Intelligence course in Bengaluru can be overwhelming. Here's a checklist to guide your selection:

1. Industry-Ready Curriculum

The course should be designed in collaboration with industry experts and regularly updated to reflect the latest AI trends.

2. Project-Based Learning

Opt for programs that emphasize project work. The more real-world experience you gain, the more industry-ready you'll be.

3. Experienced Faculty

Check if the instructors have a solid background in AI research, corporate experience, or both.

4. Placement Assistance

Many reputed institutions in Bengaluru offer dedicated placement support, resume building sessions, mock interviews, and access to hiring partners.

5. Certification

A recognized certificate can boost your resume, especially when it’s from a globally acknowledged platform or partnered with leading tech firms.

Online vs. Offline AI Courses in Bengaluru

While the charm of offline learning includes face-to-face mentoring and networking, online AI courses in Bengaluru have gained popularity due to their flexibility and affordability.

Online Courses:

Ideal for working professionals

Learn at your own pace

Often more budget-friendly

Access to recorded lectures and remote mentorship

Offline Courses:

Better peer collaboration and networking

Structured schedule and better discipline

Immediate access to labs and equipment

Some institutes even offer hybrid models, giving students the best of both worlds.

Career Opportunities After Completing an AI Course

The demand for AI professionals in Bengaluru is at an all-time high. Upon completing an AI course, you’ll be equipped to take on roles such as:

Common Job Roles:

Artificial Intelligence Engineer

Machine Learning Engineer

Data Scientist

AI Researcher

Business Intelligence Developer

Computer Vision Engineer

Robotics Engineer

NLP Engineer

Salary Expectations:

Entry-level AI roles in Bengaluru offer salaries ranging from ₹6 to ₹12 LPA.

With 3–5 years of experience, AI professionals can expect salaries upwards of ₹18–25 LPA.

Senior roles and specialized AI experts can earn ₹35 LPA and beyond.

How to Make the Most of Your AI Training in Bengaluru?

1. Engage in AI Meetups and Hackathons

Bengaluru has an active tech community. Join AI meetups, attend conferences, and participate in hackathons to build connections and gain experience.

2. Work on Real Projects

Try to apply your learning by working on real datasets. Many companies in Bengaluru offer internships to students from AI courses.

3. Build a Portfolio

Create a portfolio showcasing your projects on GitHub or a personal website. A strong portfolio can be the deciding factor during job applications.

4. Stay Updated

AI is a fast-evolving field. Keep yourself updated through research papers, newsletters, and podcasts related to Artificial Intelligence and Machine Learning.

Best AI Courses in Bengaluru in 2025

While there are numerous options, certain training providers stand out due to their placement support, hands-on learning, and reputation. One such reputed name offers AI courses with real-world projects, experienced faculty, and dedicated career support. They also emphasize applied learning and have industry partnerships that help learners land jobs in top companies across India.

Look for a program that provides:

Mentorship by AI experts

A structured, modular curriculum

Industry certifications

Strong alumni outcomes in Bengaluru's tech companies

Final Thoughts

Enrolling in aMachine Learning course in Bengaluru is not just about acquiring a new skill—it's about future-proofing your career. With the city's dynamic tech ecosystem, unparalleled learning resources, and booming job market, Bengaluru offers the perfect Launchpad for anyone looking to make a mark in the field of AI.

Whether you're aiming to become an AI engineer, transition from a non-tech background, or add AI proficiency to your current role, the opportunities are immense. Choose the right course, commit to continuous learning, and you’ll be on the path to building impactful, cutting-edge AI solutions that shape the future.

[sathya32]

How Much Time Does It Take to Learn Data Science?

Hi everyone. Data science is one of the most exciting and in-demand careers today. From predicting trends to helping companies make better decisions, data scientists play a big role in our data-driven world. But many people wonder, how long does it take to learn data science?

The answer depends on a few things—your background, how much time you can commit, and what level you want to reach.

If You’re Starting from Scratch

If you’re completely new—no coding, no math background—learning data science may take about 12 to 18 months. This includes learning Python or R, basic statistics, data visualization, and machine learning. You’d also need to understand how to work with data using tools like Pandas, NumPy, and SQL. If you want to advance your career at the Data Science Course in Chennai, you need to take a systematic approach and join up for a course that best suits your interests and will greatly expand your learning path.

Spending 10–15 hours a week studying can help you reach a beginner-to-intermediate level within a year.

If you already know programming or statistics, you can learn faster. Many people with a tech background can pick up data science in 6 to 9 months, especially if they focus on real-world projects and hands-on practice.

Key Areas to Learn

To become a data scientist, you'll need to cover topics such as

Programming: Usually Python or R

Math & Statistics: For understanding patterns and building models

Data Analysis: Using tools like Excel, Pandas, and SQL

Machine Learning: Algorithms like decision trees and regression

Data Visualization: Tools like Matplotlib, Seaborn, or Tableau

With the aid of Best Online Training & Placement programs, which offer comprehensive training and job placement support to anyone looking to develop their talents, it’s easier to learn this tool and advance your career. Learning Paths

There are many ways to learn data science:

Online Courses: Websites like Coursera, edX, and Udemy

Bootcamps: Fast-paced programs (3–6 months full-time)

University Degrees: More in-depth but takes 2–4 year

Self-study: Flexible, but requires discipline

Conclusion

Learning data science is a journey, not a race. It can take anywhere from 6 months to 2 years, depending on your pace. The best way to speed up your learning is to build projects, join communities, and stay curious.

So don’t worry about how long it takes. Focus on steady progress, and you’ll get there before you know it!

[cleveredlearning]

Introducing the Artificial Intelligence Lab for Schools in Dubai by Clevered

In an era dominated by rapid technological advancements, education must evolve to equip students with the skills and mindsets necessary for the future. The United Arab Emirates, particularly Dubai, has always been at the forefront of embracing innovation in education. Now, taking a transformative step forward, Clevered—a leading education innovation platform—is proud to launch the Artificial Intelligence (AI) Coding Lab for Schools across Dubai. This groundbreaking initiative is designed to introduce school students to the world of Artificial Intelligence, Machine Learning, and Data Science in a practical, engaging, and future-ready manner.

Why AI in Schools? The UAE’s Vision for Future-Ready Learning

The UAE government has consistently demonstrated its commitment to nurturing a tech-savvy generation. From appointing the world’s first Minister of State for Artificial Intelligence to integrating AI into national strategies, the emphasis on future technologies is evident.

By introducing AI at the school level, Dubai aims to:

Foster early exposure to emerging technologies

Promote computational and analytical thinking

Bridge the gap between classroom learning and real-world applications

Prepare students for careers that don’t yet exist

Clevered’s AI Coding Lab initiative aligns seamlessly with the UAE’s Artificial Intelligence Strategy 2031, ensuring students aren’t just consumers of technology—but its creators.

What is the AI Coding Lab by Clevered?

The AI Coding Lab is a cutting-edge, hands-on learning environment designed for school students aged 10 to 18. It integrates Artificial Intelligence, Python programming, Robotics, Machine Learning, and Data Analytics into a dynamic, age-appropriate curriculum. Unlike traditional computer labs, this lab encourages creativity, experimentation, and critical thinking.

Each lab is equipped with:

High-performance computers optimized for AI applications

Interactive smart boards and AR/VR capabilities

AI development kits (including Raspberry Pi, Arduino, and sensors)

Coding software and tools (Scratch, Python, TensorFlow Lite for kids)

Curriculum Highlights: Learning with Purpose

Clevered has developed a proprietary, project-based curriculum tailored to different age groups. The curriculum is co-designed with global AI experts and aligned with international frameworks such as ISTE (International Society for Technology in Education) and UNESCO's AI education guidelines.

Modules Include:

Introduction to AI & Ethics: Understanding how AI impacts society and the importance of responsible AI.

Python for Beginners: Learning coding basics and syntax through games and interactive challenges.

Machine Learning Projects: Creating models that recognize images, text, and patterns using supervised learning techniques.

AI in Daily Life: Exploring how AI powers voice assistants, recommendation engines, and self-driving technology.

Data Science for Kids: Learning how to clean, visualize, and draw insights from real datasets.

Robotics with AI: Programming robots to react to human voice, gestures, and environmental stimuli.

Each module concludes with a hands-on project such as building a chatbot, designing a traffic prediction model, or creating an AI-powered recycling robot.

Empowering Educators: Training & Certification

At the heart of any successful educational initiative is the educator. Clevered supports schools not just with infrastructure, but with comprehensive teacher training programs. Teachers are trained in AI pedagogy, classroom integration techniques, and continuous assessment tools.

Upon completion, educators receive:

Certified AI Educator Credentials

Ongoing professional development

Access to a global educator community

Resources for classroom implementation

Clevered also organizes AI Bootcamps and Hackathons to foster cross-school collaboration and healthy competition.

How Schools Benefit: More Than Just a Lab

The AI Coding Lab offers far-reaching benefits that extend beyond the lab walls.

1. Academic Excellence

Integrating AI projects into core subjects enhances STEM education and improves student engagement and performance.

2. Future Skills Development

Students gain foundational skills in programming, data literacy, critical thinking, and design thinking—skills identified as essential for future jobs by the World Economic Forum.

3. School Differentiation

Schools that adopt the AI Lab gain a competitive edge in attracting tech-forward families and meeting KHDA innovation standards.

4. Industry Collaboration

Clevered partners with leading technology companies, enabling students to work on real-world AI problems and internships in the future.

5. Innovation Showcases

Annual AI exhibitions and showcase days provide students with a platform to demonstrate their projects to parents, educators, and tech leaders in Dubai.

Student Success Stories: Empowering Young Innovators

In pilot programs conducted across select schools in Dubai, the results have been transformative:

Sarah, 13, created an AI-based chatbot to assist visually impaired users in navigating websites using voice commands.

Omar, 16, developed a basic fraud detection model for online transactions during a school project.

Ayesha, 14, won second place in a regional AI Hackathon using Clevered’s lab tools to create a smart attendance system using facial recognition.

These success stories reflect the real-world relevance and empowerment offered by the AI Coding Lab.

Making AI Accessible to All

Clevered believes in inclusivity and accessibility. The AI Labs are designed to be modular and scalable, allowing schools of all sizes and budgets to implement them without compromising quality. From compact setups for small classrooms to full-fledged innovation hubs, the lab solutions are customizable.

Additionally, Clevered offers AI Learning Kits for students to continue experimenting at home, ensuring learning doesn’t stop at the school gate.

Join the Movement: AI for Every School in Dubai

Dubai is not just embracing the future—it's leading it. The AI Coding Lab by Clevered is more than an educational tool; it’s a catalyst for systemic transformation in how students learn, think, and innovate.

If your school shares this vision, now is the time to act.

Partner with Clevered and bring the power of Artificial Intelligence into your classrooms.

Contact Us Today

Website: www.clevered.com

Email: info@clevered.com

Phone: + 973 36805659

Social Media: @CleveredAI | #AIinSchoolsUAE | #CleveredLabsDubai

Empower your students. Future-proof your curriculum. Let’s build tomorrow, today—with Clevered’s AI Lab for Schools.

[thirdeyedata]

Low Code/No Code Platforms: The Fastest Path to AI ROI

Artificial Intelligence is no longer a futuristic concept—it’s a core driver of innovation, efficiency, and growth across industries. Whether it's a startup looking to disrupt a market or a global enterprise optimizing operations, AI holds the potential to transform businesses from the ground up. But building AI-powered solutions from scratch often demands a heavy investment in skilled talent, infrastructure, and time. For companies that want quick ROI from AI projects, Low Code/No Code (LCNC) platforms have emerged as a game-changing alternative. These tools empower both technical and non-technical users to develop, deploy, and iterate AI applications rapidly—without writing complex code or spending months in development cycles.

Why Businesses Are Turning to Low Code/No Code for AI Success

The demand for digital transformation has led to the rise of LCNC platforms that make app development more inclusive and accessible. LCNC platforms offer drag-and-drop interfaces, pre-built AI models, API integrations, and visual logic tools that enable ‘citizen developers’—employees with limited or no coding background—to contribute to AI innovation. According to Gartner, by 2026, over 80% of users of LCNC platforms will be from departments outside traditional IT, signaling a significant democratization of AI capabilities. The LCNC market is projected to surge from $10.46 billion in 2024 to $82.37 billion in 2034 at a staggering CAGR of 22.92%, highlighting the technology’s massive growth potential.

Benefits of Using Low Code/No Code for AI App Development

Accelerated Development Timelines

LCNC platforms significantly reduce the time it takes to go from idea to implementation. Since users can create applications through graphical interfaces and reusable templates, development cycles are slashed from months to mere days or even hours. This speed enables faster experimentation, iteration, and decision-making, which is especially critical in AI projects where testing different models quickly can lead to better performance and insights.

Lower Development Costs

Building AI solutions traditionally involves hiring data scientists, machine learning engineers, backend developers, and DevOps teams. LCNC removes much of this burden by offering ready-to-use AI components and managed infrastructure. Businesses can save substantially on labor and IT costs while still deploying robust AI applications.

Greater Flexibility and Adaptability

One of the major strengths of LCNC platforms is their adaptability. Developers and non-developers alike can integrate the latest large language models, predictive analytics, and machine learning APIs directly into their applications. This flexibility allows teams to keep up with the fast-evolving AI landscape without requiring a full technical overhaul every few months.

Improved Team Productivity

By removing the bottlenecks associated with hand-coded development, LCNC tools enable more employees to participate in digital transformation. Manual and repetitive tasks such as data preprocessing, cleaning, and basic modeling can be automated, freeing up teams to focus on strategy, innovation, and customer experience.

Enhanced Collaboration Across Departments

LCNC fosters collaboration between business units and IT teams. Product managers, marketers, HR personnel, and operations teams can contribute directly to application development, while IT teams provide oversight to ensure security, performance, and compliance.

Real-World Use Cases of LCNC in AI App Development

Customer Service and Support

LCNC platforms can be used to develop AI-powered chatbots and virtual assistants that handle customer queries, process orders, and provide personalized recommendations. These solutions are easy to build using pre-integrated natural language processing (NLP) modules and can drastically improve customer satisfaction while reducing support costs.

Supply Chain Optimization

Businesses in logistics and supply chain can use LCNC tools to build AI models that forecast demand, manage inventory, optimize routes, and prevent disruptions. By visualizing supply chain data and training models within a no-code platform, operations managers can respond to real-time challenges without depending on a dedicated data science team.

Human Resources and Recruitment

HR teams can leverage LCNC to automate resume screening, conduct sentiment analysis on interviews, or create training programs tailored to employee learning patterns. By using AI features available in LCNC platforms, HR departments can save time and improve the quality of hiring decisions.

Financial Services

Banks and financial institutions use LCNC to build AI-driven applications for fraud detection, credit scoring, and personalized financial planning. These platforms allow analysts and advisors to prototype solutions quickly, test with real data, and iterate without getting bogged down by traditional development timelines.

Healthcare and Life Sciences

From patient data processing to predictive diagnostics and drug discovery, LCNC platforms are transforming how healthcare providers use AI. Medical professionals can build applications that analyze clinical notes, detect anomalies, or streamline administrative tasks, all without writing a single line of code.

Strategies to Maximize Success with LCNC Platforms

Identify High-Impact Use Cases

Not all AI projects yield the same return on investment. Start by identifying use cases where AI can bring tangible benefits—like cost savings, time reduction, or enhanced customer engagement. Prioritize projects that align with key business goals and have available data to support model development.

Empower Citizen Developers with Training

While LCNC platforms simplify development, it's essential to train employees on platform functionality, responsible data handling, and basic AI principles. This empowers users to create meaningful solutions while minimizing the risk of flawed logic or data misuse.

Ensure Strong IT Oversight and Governance

Though LCNC platforms allow broader participation, IT departments still play a crucial role. They must oversee architecture decisions, manage integration with enterprise systems, and enforce security protocols. Data governance policies should be in place to ensure AI applications comply with legal and ethical standards.

Integrate Ethics Into AI Projects

As AI becomes more integrated into decision-making, ethical considerations must take center stage. LCNC developers should be trained to avoid biases, respect user privacy, and ensure transparency in how AI decisions are made and communicated.

Maintain Iterative Development and Feedback Loops

The best AI applications are built with continuous feedback and iteration. LCNC platforms support this by enabling quick updates and versioning. Teams should collect user feedback early and often, refining models and interfaces to enhance usability and performance.

Leading LCNC Platforms for AI Integration

Several major tech companies have invested in building LCNC platforms with powerful AI integration capabilities. Microsoft Power Platform, for instance, allows users to create applications using Power Apps and integrate Azure AI services seamlessly. Salesforce’s Einstein AI provides predictive analytics and machine learning models that work within its no-code CRM environment. ServiceNow also offers LCNC capabilities for workflow automation infused with AI, ideal for enterprise environments.

These platforms exemplify the convergence of business tools and AI, making advanced capabilities available without the overhead of traditional software development cycles.

The Future of LCNC and AI in Business

As the LCNC landscape continues to mature, we can expect even more advanced AI integrations, including fine-tuning large language models, deploying computer vision pipelines, and managing edge AI applications—all from simplified interfaces. Open-source no-code platforms are also emerging, providing even more customization for businesses with specific requirements. In the long term, the combination of LCNC and AI could lead to a future where building intelligent applications is as intuitive as creating a PowerPoint presentation.

Moreover, the speed at which these platforms are evolving means businesses that adopt early can continuously outpace competitors. They’ll be better positioned to experiment, learn, and adapt to market changes using tools that democratize innovation.

Conclusion: Build Smart, Fast, and Scalable AI Solutions with LCNC

In a business environment that demands agility and intelligence, LCNC platforms serve as the catalyst that makes AI adoption scalable and impactful. From reducing time to market to lowering development costs and empowering non-technical teams, these platforms are redefining how organizations innovate with AI. Whether your goal is to automate manual workflows, improve customer experience, or create entirely new digital services, LCNC offers a compelling path forward.

Are you ready to transform your business using AI automation for industry use cases? Partner with a technology expert who understands both the strategic and technical sides of low-code and no-code development. The future of AI is accessible—embrace it with LCNC.

[carlhofelina]

Don't Hire, Outsource: The Future of SaaS Development

For a new business aiming for strong growth, especially one offering Software as a Service (SaaS), a smart move is getting help from outside experts to build your software. Your core team has great ideas about what customers need and how to get them, but building the software can take a lot of their time. 

By working with experienced software builders, you get their skills quickly, making the process faster and ensuring your software is well-made and can handle many users. This can also save money compared to hiring a big team yourself, letting you focus on understanding your customers and growing your business. Think of it as adding skilled helpers so your main team can focus on what's most important for your startup's success.

What Is Saas Development & How Is It Important?

SaaS development is the process of creating software applications hosted online (in the "cloud"), and you access them through the internet, usually via a web browser or a mobile app. Think of it like renting software instead of buying it outright. You typically pay a recurring fee (monthly or yearly) to use the software.

For your small business, embracing SaaS – either by using existing SaaS tools or by developing your own – offers significant advantages:

Lower Upfront Costs: Unlike traditional software that requires a big one-time purchase, most SaaS solutions have subscription models. This means you can start using powerful tools without a large initial investment, freeing up your precious capital for other needs.

Accessibility from Anywhere: You and your team can access SaaS applications from any device with an internet connection – be it your office computer, your laptop at home, or even your smartphone while on the go. This flexibility supports remote work and keeps everyone connected.

Automatic Updates and Maintenance: Forget about manual software updates and the hassle of IT maintenance. SaaS providers handle all of this automatically, ensuring you always have the latest features and security patches without any disruption to your work.

Integration with Other Tools: Many SaaS applications are designed to work well with other online tools, streamlining your workflows and improving efficiency for your business operations.

Access to Advanced Features (Including Potential for Machine Learning): Depending on your needs, you can access sophisticated features, and if your SaaS startup ideas involve cutting-edge technologies like machine learning, building a SaaS platform can be the most effective way to deliver those capabilities to your users.

Benefits of Outsourcing Saas Development

Launch Your SaaS Startup Ideas Faster: By tapping into readily available external experts, you can significantly speed up your development process. This means you can get your innovative software to market quicker and start generating revenue sooner, without the delays of building an in-house team from scratch.

Manage Your Budget Wisely for Startup Growth Strategies: For your small business, every peso counts. Outsourcing gives you access to specialized skills at a more predictable and often lower cost than hiring a full-time team. This frees up crucial capital that you can then invest in essential startup growth strategies like marketing and reaching your target customers.

Access the Latest Tech for Your SaaS Startup Ideas: The tech world moves fast. When you outsource, you gain access to professionals with experience in diverse and cutting-edge technologies. This is especially beneficial if your SaaS startup ideas involve advanced features, perhaps even elements of a machine learning startup, ensuring your platform is built with modern tools and best practices.

Focus Your Energy on Your Core Business and Startup Growth Strategies: You have a vision for your SaaS business. Let external experts handle the complexities of software development so you can concentrate on what you do best: understanding your customers, refining your business model, and implementing effective startup growth strategies to capture your market.

Scale Your Operations Flexibly as Your SaaS Startup Ideas Evolve: As your small business grows and your SaaS startup ideas take shape, you'll need flexibility. Outsourcing partners can often adjust their teams to match your changing needs, providing the scalability you require without the long-term commitments of permanent hires.

Reduce Development Risks for Your SaaS Startup: Building software can be challenging. Experienced outsourcing teams often have established processes and quality control measures, which can help you mitigate potential development risks and ensure a more stable and reliable product for your users.

Find the Right Expertise for Your Specific SaaS Startup Needs (Including Machine Learning): Whether your SaaS startup requires expertise in a particular programming language, platform, or even specialized areas like machine learning for a niche market, outsourcing connects you with professionals who have the exact skills you need.

Gain a Broader Perspective for Your Startup Growth Strategies: Working with international outsourcing partners can expose your small business to global best practices and innovative approaches, potentially informing your startup growth strategies and helping you think beyond your initial target market for future expansion.

Are You Ready For An Upgrade?

Outsourcing your SaaS development is both a wise and smart move as this provides your small business with access to specialized skills and faster development, allowing you to launch your innovative software solutions more quickly and cost-effectively.

Visit our Best Virtual Specialist to know more about our skilled virtual specialists who are ready to help your small business upgrade it to the next level.

References:

https://www.yansmedia.com/blog/in-house-animation-vs-outsourcing-cost-comparison

https://www.linkedin.com/posts/nine-hertz-india-pvt-ltd-_saas-application-development-types-benefits-activity-7321435207176265728-mVKK

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Which library supports deep learning?

The library most commonly used to support deep learning is TensorFlow. Developed by Google Brain, TensorFlow is an open-source library that allows developers to build and train deep learning models efficiently. It supports both CPU and GPU computation and is highly scalable, making it suitable for both research and production environments. TensorFlow is often used for tasks such as image recognition, natural language processing, and time-series prediction. It provides a flexible architecture for creating neural networks, allowing users to design, train, and deploy machine learning models.

Another popular deep learning library is PyTorch, developed by Facebook. PyTorch is particularly favored in academic and research settings due to its dynamic computation graph, which makes debugging easier and more intuitive. It provides a high-level interface for building and training models, and its seamless integration with Python makes it highly popular for rapid prototyping and experimentation.

Both TensorFlow and PyTorch support a wide range of neural network architectures, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and more. They are equipped with high-level APIs such as Keras (in TensorFlow) and Torchvision (in PyTorch) that abstract away much of the complexity, making them easier to use for beginners while still powerful enough for advanced users.

For deep learning practitioners, both libraries also offer various pre-trained models that can be fine-tuned for specific tasks, making it possible to leverage large datasets without needing to train models from scratch.

By taking a data science and machine learning course, you will gain hands-on experience with these libraries, learning how to effectively implement deep learning models to solve complex problems in various fields like healthcare, finance, and technology.