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innovationalofficesolution · 9 days ago

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Aligning BI Strategy with Microsoft’s Analytics Stack

In today’s data-driven world, aligning your Business Intelligence (BI) strategy with a robust analytics ecosystem is no longer optional—it’s essential. Microsoft’s analytics stack, centered around Power BI, Azure Synapse Analytics, and the broader Azure Data Services, offers a scalable, unified platform that can transform how organizations gather insights, make decisions, and achieve business goals.

For enterprises transitioning from Tableau to Power BI, integrating with Microsoft’s analytics stack is more than a technical shift—it’s a strategic opportunity.

Why Microsoft’s Analytics Stack?

Microsoft’s stack is designed with synergy in mind. Power BI serves as the front-end visualization tool, while Azure Synapse Analytics acts as the powerhouse for data integration, big data analytics, and real-time processing. Azure Data Factory, Azure Data Lake, and SQL Server complement the environment by enabling seamless data movement, storage, and management.

Aligning with this ecosystem empowers organizations to:

Unify data access and governance

Leverage native AI and machine learning

Streamline collaboration via Microsoft 365 integration

Improve performance with cloud-scale analytics

Key Considerations for BI Strategy Alignment

1. Define Strategic Goals Clearly Start with identifying what you want to achieve—whether it’s real-time reporting, predictive analytics, or better self-service BI. Microsoft’s platform offers the flexibility to scale BI initiatives based on maturity and business priorities.

2. Optimize Data Architecture Unlike Tableau’s more visual-centric architecture, Power BI thrives in a model-driven environment. Organizations should design dataflows and models to fully leverage Power BI’s DAX capabilities, semantic layers, and integration with Azure SQL and Synapse.

3. Leverage Azure Synapse for Enterprise-Scale Analytics Synapse enables unified analytics over big data and structured data. When aligned with Power BI, it removes data silos and allows for direct querying of large datasets, which enhances performance and reduces duplication.

4. Automate with Azure Data Factory A well-aligned BI strategy includes efficient ETL processes. Azure Data Factory helps automate pipelines and data transformations that feed clean data into Power BI for analysis, reducing manual effort and errors.

5. Prioritize Governance and Security With Microsoft Purview and Power BI's Row-Level Security (RLS), organizations can ensure data compliance and user-level control over access. This becomes increasingly vital during and after a migration from platforms like Tableau.

A Strategic Migration Opportunity

For those moving from Tableau to Power BI, aligning with Microsoft’s full analytics stack opens doors to advanced capabilities previously underutilized. Tools like Pulse Convert by OfficeSolution help automate and optimize this migration process, ensuring that your data assets, dashboards, and logic align smoothly with Power BI’s architecture.

Final Thoughts

Aligning your BI strategy with Microsoft’s analytics stack isn't just a move to a new tool—it’s an investment in a future-ready, scalable, and intelligent data ecosystem. Whether you're migrating from Tableau or building from scratch, OfficeSolution is here to guide you in leveraging the full potential of Microsoft's platform for long-term analytics success.

#tableautopowermigration

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skygola · 23 days ago

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IT Company in Mumbai: Driving Digital Transformation in India’s Financial Capital

Mumbai, often referred to as the financial capital of India, is rapidly emerging as a major technology hub. From multinational corporations to startups, businesses across sectors rely on innovative IT solutions to stay competitive in the digital age. This growing demand has led to the rise of numerous world-class IT company in Mumbai, offering services that power digital transformation across industries.

Why Choose an IT Company in Mumbai?

Choosing an IT company in Mumbai gives you access to a dynamic pool of tech talent, modern infrastructure, and a business-friendly ecosystem. Here’s why Mumbai is a preferred destination for IT services:

Skilled Workforce: Mumbai is home to top engineering institutes and tech universities, producing highly skilled software developers, data analysts, and IT professionals.

Strategic Location: Its connectivity and proximity to other economic hubs make Mumbai a strategic base for tech operations.

Diverse Industry Expertise: IT companies in Mumbai serve a wide range of industries, including finance, healthcare, retail, media, real estate, and more.

Innovation-Driven Environment: The city hosts numerous tech incubators, accelerators, and startup ecosystems that foster innovation and digital advancement.

Core Services Offered by IT Companies in Mumbai

Software Development

Custom application development

Enterprise software solutions (ERP, CRM)

Mobile app development (Android, iOS)

Web Development & Design

Website design and redesign

E-commerce platforms

UX/UI development

IT Consulting & Strategy

Digital transformation roadmaps

Technology audits

Business process optimization

Cloud Solutions

Cloud migration and integration

SaaS, PaaS, and IaaS deployment

Amazon Web Services (AWS), Microsoft Azure, Google Cloud support

Cybersecurity

Network security

Data protection and compliance

Penetration testing and threat monitoring

Managed IT Services

Infrastructure management

IT helpdesk support

Network and server maintenance

Data Analytics & AI Solutions

Big data analysis

Business intelligence dashboards

Machine learning model development

Digital Marketing & SEO

Search engine optimization

Pay-per-click advertising

Social media management and content marketing

Industries Served by Mumbai-Based IT Companies

Banking & Finance

Healthcare & Life Sciences

Retail & E-Commerce

Logistics & Supply Chain

Real Estate & Construction

Media & Entertainment

Government & Public Sector

How to Choose the Right IT Company in Mumbai

When selecting an IT partner, look for the following:

Relevant Industry ExperienceCertified Professionals (e.g., Microsoft, AWS, Google)Strong Client Portfolio & Case StudiesEnd-to-End Support (Development, Testing, Deployment, Maintenance)Transparent Pricing & Agile Methodology

Top Locations for IT Companies in Mumbai

Andheri East & SEEPZ – Known for tech parks and MNC offices

Navi Mumbai (Vashi, Belapur) – Emerging as a tech and business hub

Powai & Hiranandani – Popular with startups and product-based companies

BKC (Bandra-Kurla Complex) – Preferred for enterprise-level IT solutions

Future of IT in Mumbai

With smart city initiatives, expanding data infrastructure, and increasing adoption of AI and cloud technologies, Mumbai is poised to become a major player in the global IT services market. The city’s IT companies are not just service providers—they are strategic partners in digital transformation.

Conclusion

An experienced IT company in Mumbai can provide scalable, secure, and innovative technology solutions tailored to your business needs. Whether you’re a startup aiming to launch your first app or a large enterprise looking to modernize your IT infrastructure, Mumbai offers a rich ecosystem of talent and technology to help you succeed.

#IT company in Mumbai

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elephantintheboardroom16 · 2 months ago

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1. Data Strategy and Consulting

We start by understanding your business goals, challenges, and the data you currently possess. Our experts develop a clear, actionable data strategy to help you organize, manage, and utilize data effectively. This foundation is essential for successful AI and analytics initiatives.

2. Data Engineering

Strong data infrastructure is critical. We design, build, and maintain robust data pipelines and storage solutions, ensuring your data is secure, clean, and ready for analysis. Whether it's cloud migration, big data processing, or integrating diverse data sources, we make your data architecture future-ready.

3. AI and Machine Learning Solutions

From predictive analytics to natural language processing and computer vision, we implement AI models that empower businesses to automate tasks, predict outcomes, and personalize experiences. Our solutions are designed to be scalable, ethical, and aligned with your organizational values.

4. Business Intelligence and Analytics

Using cutting-edge BI tools and advanced analytics techniques, we transform complex datasets into intuitive dashboards and reports. We help you monitor KPIs, track business performance, and make data-driven decisions with confidence.

5. Data Governance and Compliance

In a world of increasing data regulations, staying compliant is essential. Our data governance solutions ensure that your data practices meet legal standards and industry best practices, protecting your business and your customers.

6. AI Product Development

We collaborate with businesses to conceptualize, design, and build AI-powered products that solve real-world problems. From AI chatbots to recommendation systems, we create solutions that drive customer engagement and operational excellence.

looking for AI driven data solutions ,visit link mentioned above.

#seo services #advertising #ai #marketing #sem

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williambutcher008 · 2 months ago

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Redefining Intelligence: How We Help Businesses Harness the Power of AI and Data

Empowering Tomorrow’s Decisions with NTT DATA

In the modern digital economy, intelligence is no longer just about human insight—it’s about how seamlessly organizations can integrate artificial intelligence (AI) and data into their decision-making fabric. From predictive algorithms to autonomous systems, businesses today are challenged not just to access data, but to extract intelligence that can lead to better, faster, and more sustainable decisions.

At NTT DATA, we believe intelligence isn’t static—it’s evolving. And we’re helping businesses around the world redefine what intelligence means in the age of AI and big data.

Why Redefining Intelligence Matters

Data is growing exponentially—by 2025, global data creation is expected to reach 181 zettabytes. But data alone doesn’t deliver value. Intelligence emerges when data is refined, contextualized, and applied to real-world problems.

The organizations that will lead the future are those who can transform:

Raw data into actionable insights

Insights into automated decisions

Decisions into business value

That’s where NTT DATA comes in.

Our Mission: Transforming Data into Intelligent Action

At NTT DATA, we help businesses go beyond traditional analytics and build a culture of decision intelligence powered by AI. Whether you’re in finance, healthcare, retail, or manufacturing, we deliver AI and data-driven solutions tailored to your unique challenges and opportunities.

We don’t just build systems.

We build intelligent ecosystems that continuously learn, adapt, and deliver results.

How We Redefine Intelligence for Enterprises

Here’s how we support businesses at every step of their AI and data journey:

1. Data Modernization & Governance

Before intelligence can be generated, your data must be trusted. We help organizations clean, consolidate, and modernize their data infrastructure.

Data lake creation & integration

Real-time data pipelines

Data governance & compliance frameworks

Migration to cloud-native environments

This foundation allows AI to be trained on high-quality data—maximizing performance and relevance.

2. AI & Machine Learning Solutions

From intelligent automation to advanced forecasting models, we help businesses deploy AI that’s not just smart—but strategically aligned.

Natural Language Processing (NLP) for chatbots and sentiment analysis

Computer Vision for visual inspection and anomaly detection

Predictive analytics for demand forecasting and risk scoring

Reinforcement learning for optimization and autonomous systems

We ensure that AI becomes an extension of your workforce—not just a black-box tool.

3. Embedded Decision Intelligence

We integrate AI insights into your business workflows, apps, and dashboards—so decision-makers can act faster and with greater confidence.

AI-augmented BI dashboards

Recommendation engines

KPI-driven decision models

Self-service AI for business users

Decision-making no longer lives in silos—it lives at every level of your organization.

4. AI-Powered Automation

We combine AI with Robotic Process Automation (RPA) to take operational efficiency to the next level.

Intelligent document processing

Automated customer service

Workflow optimization

Cognitive bots for back-office operations

This means fewer manual tasks, lower costs, and faster execution at scale.

Real-World Impact: Where AI Meets Business Value

Let’s look at how we’ve helped global companies turn intelligence into impact:

🏥 Healthcare: Predictive Patient Care

We partnered with a hospital chain to deploy AI that predicts patient deterioration risks 12 hours before traditional methods. This helped reduce emergency escalations by 22% and improved care delivery.

🏭 Manufacturing: Quality Control with Computer Vision

For a leading manufacturer, we used AI-powered cameras to detect product defects in real-time. This resulted in a 30% reduction in waste and improved customer satisfaction.

🏦 Banking: Fraud Detection in Real-Time

We helped a financial institution build a fraud detection engine that analyzes transactions in milliseconds. It now catches 98% of fraudulent activities before they complete.

🛍️ Retail: Personalized Customer Journeys

With our AI recommendation engine, an e-commerce retailer increased repeat purchases by 40% and grew revenue by 25% year-over-year.

These aren’t just success stories—they’re examples of redefined intelligence in action.

Why Businesses Trust NTT DATA

Choosing the right AI partner is about more than tools—it’s about trust, execution, and long-term vision. Here’s why organizations across 50+ countries choose NTT DATA:

✅ Global Scale, Local Expertise

Our global reach means we bring diverse experience across industries, while offering regional support for compliance, localization, and customization.

✅ Cross-Industry Experience

From telecom to insurance to public sector, we’ve delivered AI success across a broad spectrum of industries.

✅ Strategic Partnerships

We collaborate with tech leaders like Microsoft, AWS, Google Cloud, NVIDIA, and SAP—so you always get best-in-class solutions.

✅ Focus on Responsible AI

Our AI is built on principles of fairness, transparency, and security. Every model we deploy is explainable, auditable, and ethically designed.

What Sets Our AI Approach Apart?

At NTT DATA, we see AI as a strategic enabler—not just a technology trend. Our approach focuses on:

Human-AI collaboration We design AI to complement human skills, not replace them.

Data contextualization We don’t just process data—we understand the “why” behind it.

Iterative learning models Our systems improve continuously through feedback loops.

Tailored AI maturity roadmaps We meet you where you are and guide you to where you need to be.

The Future of Intelligence Is Here

The next decade of business will be defined not by size or speed, but by intelligence—how well you can predict, personalize, and pivot in real-time.

AI and data are the backbone of this transformation. But success doesn’t come from tools alone. It comes from a trusted partner that understands your business and can tailor intelligence to your goals.

That’s what we do at NTT DATA.

Ready to Redefine Intelligence for Your Business?

Whether you’re just starting your AI journey or scaling enterprise-wide data strategies, we’re here to help.

🔗 Explore how we help global organizations harness data and AI: 👉 https://www.nttdata.com/global/en/

📩 Let’s talk about how we can make your business smarter—one decision at a time.

Final Thought

Intelligence is no longer a luxury—it’s a competitive necessity. Redefine it with NTT DATA, and unlock a future where your business decisions are guided not just by instinct, but by insight.

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kadellabs69 · 4 months ago

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Kadel Labs: Leading the Way as Databricks Consulting Partners

Introduction

In today’s data-driven world, businesses are constantly seeking efficient ways to harness the power of big data. As organizations generate vast amounts of structured and unstructured data, they need advanced tools and expert guidance to extract meaningful insights. This is where Kadel Labs, a leading technology solutions provider, steps in. As Databricks Consulting Partners, Kadel Labs specializes in helping businesses leverage the Databricks Lakehouse platform to unlock the full potential of their data.

Understanding Databricks and the Lakehouse Architecture

Before diving into how Kadel Labs can help businesses maximize their data potential, it’s crucial to understand Databricks and its revolutionary Lakehouse architecture.

Databricks is an open, unified platform designed for data engineering, machine learning, and analytics. It combines the best of data warehouses and data lakes, allowing businesses to store, process, and analyze massive datasets with ease. The Databricks Lakehouse model integrates the reliability of a data warehouse with the scalability of a data lake, enabling businesses to maintain structured and unstructured data efficiently.

Key Features of Databricks Lakehouse

Unified Data Management – Combines structured and unstructured data storage.

Scalability and Flexibility – Handles large-scale datasets with optimized performance.

Cost Efficiency – Reduces data redundancy and lowers storage costs.

Advanced Security – Ensures governance and compliance for sensitive data.

Machine Learning Capabilities – Supports AI and ML workflows seamlessly.

Why Businesses Need Databricks Consulting Partners

While Databricks offers powerful tools, implementing and managing its solutions requires deep expertise. Many organizations struggle with:

Migrating data from legacy systems to Databricks Lakehouse.

Optimizing data pipelines for real-time analytics.

Ensuring security, compliance, and governance.

Leveraging machine learning and AI for business growth.

This is where Kadel Labs, as an experienced Databricks Consulting Partner, helps businesses seamlessly adopt and optimize Databricks solutions.

Kadel Labs: Your Trusted Databricks Consulting Partner

Expertise in Databricks Implementation

Kadel Labs specializes in helping businesses integrate the Databricks Lakehouse platform into their existing data infrastructure. With a team of highly skilled engineers and data scientists, Kadel Labs provides end-to-end consulting services, including:

Databricks Implementation & Setup – Deploying Databricks on AWS, Azure, or Google Cloud.

Data Pipeline Development – Automating data ingestion, transformation, and analysis.

Machine Learning Model Deployment – Utilizing Databricks MLflow for AI-driven decision-making.

Data Governance & Compliance – Implementing best practices for security and regulatory compliance.

Custom Solutions for Every Business

Kadel Labs understands that every business has unique data needs. Whether a company is in finance, healthcare, retail, or manufacturing, Kadel Labs designs tailor-made solutions to address specific challenges.

Use Case 1: Finance & Banking

A leading financial institution faced challenges with real-time fraud detection. By implementing Databricks Lakehouse, Kadel Labs helped the company process vast amounts of transaction data, enabling real-time anomaly detection and fraud prevention.

Use Case 2: Healthcare & Life Sciences

A healthcare provider needed to consolidate patient data from multiple sources. Kadel Labs implemented Databricks Lakehouse, enabling seamless integration of electronic health records (EHRs), genomic data, and medical imaging, improving patient care and operational efficiency.

Use Case 3: Retail & E-commerce

A retail giant wanted to personalize customer experiences using AI. By leveraging Databricks Consulting Services, Kadel Labs built a recommendation engine that analyzed customer behavior, leading to a 25% increase in sales.

Migration to Databricks Lakehouse

Many organizations still rely on traditional data warehouses and Hadoop-based ecosystems. Kadel Labs assists businesses in migrating from legacy systems to Databricks Lakehouse, ensuring minimal downtime and optimal performance.

Migration Services Include:

Assessing current data architecture and identifying challenges.

Planning a phased migration strategy.

Executing a seamless transition with data integrity checks.

Training teams to effectively utilize Databricks.

Enhancing Business Intelligence with Kadel Labs

By combining the power of Databricks Lakehouse with BI tools like Power BI, Tableau, and Looker, Kadel Labs enables businesses to gain deep insights from their data.

Key Benefits:

Real-time data visualization for faster decision-making.

Predictive analytics for future trend forecasting.

Seamless data integration with cloud and on-premise solutions.

Future-Proofing Businesses with Kadel Labs

As data landscapes evolve, Kadel Labs continuously innovates to stay ahead of industry trends. Some emerging areas where Kadel Labs is making an impact include:

Edge AI & IoT Data Processing – Utilizing Databricks for real-time IoT data analytics.

Blockchain & Secure Data Sharing – Enhancing data security in financial and healthcare industries.

AI-Powered Automation – Implementing AI-driven automation for operational efficiency.

Conclusion

For businesses looking to harness the power of data, Kadel Labs stands out as a leading Databricks Consulting Partner. By offering comprehensive Databricks Lakehouse solutions, Kadel Labs empowers organizations to transform their data strategies, enhance analytics capabilities, and drive business growth.

If your company is ready to take the next step in data innovation, Kadel Labs is here to help. Reach out today to explore custom Databricks solutions tailored to your business needs.

#best databricks services

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infiniumresearch789 · 1 year ago

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Data Pipeline Tools Market Valued at US$ 62.46 Billion in 2021, Anticipating a 24.31% CAGR to 2030

In the rapidly evolving landscape of data management and analytics, data pipeline tools have emerged as indispensable assets for organizations aiming to leverage big data for strategic advantage. The global data pipeline tools market is witnessing significant growth, driven by the increasing volume of data generated across various industries, the rising adoption of cloud-based solutions, and the need for real-time data processing and analytics. This article delves into the dynamics of the global data pipeline tools market, exploring key trends, growth drivers, challenges, and future prospects.

Understanding Data Pipeline Tools

Data pipeline tools are essential components in the data management ecosystem. They enable the seamless movement of data from various sources to destinations, facilitating data integration, transformation, and loading (ETL). These tools help organizations automate the process of data collection, cleansing, and enrichment, ensuring that data is accurate, consistent, and ready for analysis.

Market Drivers

Explosive Growth of Big Data: The proliferation of digital devices, social media, IoT, and other technologies has led to an unprecedented increase in data generation. Organizations are inundated with vast amounts of data that need to be processed, analyzed, and utilized for decision-making. Data pipeline tools provide the necessary infrastructure to handle large-scale data efficiently.

Adoption of Cloud-Based Solutions: Cloud computing has revolutionized the way organizations manage their IT infrastructure. The scalability, flexibility, and cost-effectiveness of cloud-based solutions have prompted many businesses to migrate their data operations to the cloud. Data pipeline tools that are optimized for cloud environments enable seamless data integration across on-premise and cloud systems.

Real-Time Data Processing Needs: In today’s fast-paced business environment, real-time data processing has become a critical requirement. Organizations need to respond to events and make decisions based on the latest data. Data pipeline tools equipped with real-time processing capabilities allow businesses to gain timely insights and enhance their operational efficiency.

Rise of Advanced Analytics and AI: The adoption of advanced analytics and artificial intelligence (AI) technologies is driving the demand for robust data pipeline tools. These tools are essential for feeding high-quality, clean data into machine learning models and other analytical frameworks, ensuring accurate and actionable insights.

Key Trends

Shift to Managed Data Pipeline Services: As the complexity of data environments increases, many organizations are opting for managed data pipeline services. These services offer a hassle-free way to manage data pipelines, with vendors handling the infrastructure, maintenance, and support. This trend is particularly prominent among small and medium-sized enterprises (SMEs) that lack the resources to manage data pipelines in-house.

Integration with Data Lakes and Warehouses: Data pipeline tools are increasingly being integrated with data lakes and data warehouses. This integration enables organizations to store and analyze large volumes of structured and unstructured data, providing a comprehensive view of their operations. The ability to seamlessly move data between different storage solutions enhances the flexibility and scalability of data analytics workflows.

Focus on Data Governance and Compliance: With growing concerns about data privacy and regulatory compliance, organizations are placing greater emphasis on data governance. Data pipeline tools are evolving to include features that support data lineage, auditing, and compliance with regulations such as GDPR and CCPA. Ensuring data integrity and traceability is becoming a critical aspect of data management strategies.

Advancements in Automation and AI: Automation is a key trend shaping the future of data pipeline tools. Leveraging AI and machine learning, these tools can now automate complex data transformation tasks, detect anomalies, and optimize data flows. The incorporation of AI-driven features enhances the efficiency and accuracy of data pipelines, reducing the need for manual intervention.

Challenges

Data Quality Issues: Ensuring data quality remains a significant challenge for organizations. Inaccurate, incomplete, or inconsistent data can undermine the effectiveness of data analytics and decision-making processes. Data pipeline tools must incorporate robust data validation and cleansing mechanisms to address these issues.

Complexity of Data Integration: Integrating data from diverse sources, including legacy systems, cloud applications, and IoT devices, can be complex and time-consuming. Organizations need data pipeline tools that can handle heterogeneous data environments and provide seamless connectivity across various data sources.

Scalability Concerns: As data volumes continue to grow, scalability becomes a critical concern. Data pipeline tools must be able to scale efficiently to handle increasing data loads without compromising performance. This requires advanced architectures that can distribute processing tasks and optimize resource utilization.

Security and Privacy Risks: With the increasing prevalence of cyber threats, ensuring the security and privacy of data during transit and storage is paramount. Data pipeline tools must incorporate robust encryption, access control, and monitoring features to safeguard sensitive information from unauthorized access and breaches.

Read More: https://www.infiniumglobalresearch.com/market-reports/global-data-pipeline-tools-market

Future Prospects

The future of the global data pipeline tools market looks promising, with several factors contributing to its growth and evolution:

Proliferation of IoT Devices: The Internet of Things (IoT) is expected to generate massive amounts of data, further driving the demand for efficient data pipeline tools. Organizations will need robust solutions to manage and analyze IoT data in real-time, enabling new use cases in smart cities, industrial automation, and connected healthcare.

Increased Adoption of Edge Computing: Edge computing is gaining traction as organizations seek to process data closer to the source. Data pipeline tools that support edge computing will become increasingly important, enabling real-time data processing and analytics at the edge of the network.

Expansion of AI and Machine Learning: The integration of AI and machine learning into data pipeline tools will continue to advance. These technologies will enable more sophisticated data transformation, anomaly detection, and predictive analytics, enhancing the overall capabilities of data pipelines.

Growth of Data-as-a-Service (DaaS): Data-as-a-Service (DaaS) is an emerging model where organizations can access and utilize data on-demand through cloud-based platforms. Data pipeline tools will play a crucial role in enabling DaaS by providing the necessary infrastructure for data integration, transformation, and delivery.

Conclusion

The global data pipeline tools market is poised for substantial growth, driven by the increasing importance of data in business decision-making and the ongoing advancements in technology. Organizations across industries are recognizing the value of robust data pipeline solutions in harnessing the power of big data, cloud computing, and real-time analytics. As the market continues to evolve, key trends such as managed services, data governance, and AI-driven automation will shape the future of data pipeline tools, offering new opportunities and addressing emerging challenges. For businesses looking to stay competitive in the data-driven era, investing in advanced data pipeline tools will be a strategic imperative.

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stellarit · 3 years ago

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Change Tomorrow

An integrated dashboard that includes social media analytics, trends, and insights. Through its proprietary tool, Stellar Engage, ChangeTomorrow offers an efficient and robust phone banking service

https://stellarit.com/

#advanced data analytics ai & machine learning big data analysis cloud integration cloud migration data governance data warehouse virtualizat

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theartofservice · 4 years ago

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236 Center of Excellence benefits and rewards you’ll get that show you’re successful. How many can you move to ‘Done’?

You know you’ve got Center of Excellence under control when you can:

1. Address data privacy for information provided to your organization as part of the cloud excellence implementer program.

2. Empower your business users to be value creators in the data supply chain.

3. Measure the business risk of using machines to make decisions and recommendations.

4. Leverage the data that you have to provide better offers and experiences.

5. Identify sources of external data that will complement the data that you own already.

6. Align business goals and the data that exists in your organization.

7. Assess the change that has occurred and make adjustments to maximize effectiveness.

8. Effectively track and report on your research activities to provide better strategic management decisions.

9. Meet the need for business agility whilst ensuring security and compliance.

10. Classify the processes within the Strategy group, or the activities of the Business Process Center of Excellence, or the processes associated with Corporate Planning.

11. Create the data or the environments today to test use cases.

12. Protect sensitive information as sensitive PII data across your enterprise.

13. Trace the path from what you have accomplished to what you will do next.

14. Best support the development of Big Data systems.

15. As data leaders best ensure that you are fit to make the most of and avoid getting left behind.

16. Use big data to model the impact of climate change on the most vulnerable populations.

17. Transform Data into Knowledge to support decision making.

18. Ensure your technology infrastructure is scalable and can support the required business agility.

19. Integrate your enterprise and cloud applications to your data warehouse in cloud.

20. Determine whether a power user program or Center of Excellence might work well for your specific group of users.

21. Use data to optimize supply chains and make them more resilient.

22. Share data across the value chain to promote smarter consumption.

23. Accelerate the migration effort to realize the business and technology benefits more quickly.

24. Structure your internal change management organization, staff it and operate it.

25. Monetize new sources of data to new create new products and services.

26. Find only relevant data when you need it.

27. Get access to skills that can help accelerate your project without making too many mistakes.

28. Build the enterprise business case for robotic automation.

29. Ensure a data driven approach to strategic decision making.

30. Accelerate the migration effort to realize the business and technology benefits.

31. Know when everyone has turned in plans/budgets.

32. Know when everyone has turned in the plans/budgets.

33. Engage with existing online communities in support of your core business functions.

34. Create/use data display tools in development.

35. Meet the rapidly changing business demands for new applications and capabilities.

36. Achieve greater business results for your own organization.

37. Identify the key drivers of business success.

38. Identify the key operational drivers of business success.

39. Maximize the resources needed for a Data Loss Prevention initiative.

40. Get the replacement CSP to assist in the cost of data migration.

41. Plan to involve stakeholders and business units to ensure the platform is used to its best capability and purpose.

42. Ensure you are compliant from Day 1 as you start doing business in a new jurisdiction.

43. As a future focused CFO and a key strategic partner ensure that you are taking advantage of the latest and most relevant technology trends.

44. Nominate a director using the proxy access provisions of your organizations By Laws.

45. Ensure compliance with business practices and objectives.

46. Continue to secure and manage the ever growing amount of information you handle.

47. Overcome the challenges of decentralized management, multiple Business Intelligence systems, and fragmented implementations.

48. Get buy in for data and analytics initiatives.

49. Make better business decisions by effectively leveraging internal and external data.

50. Get a group of busy architects to change the way they work.

51. Best present this information to enhance understanding and use.

52. Make a bigger impact on business results.

53. Get Buy-In for Data and Analytics Initiatives.

54. Create a powerful brand based on data and evidence.

55. Govern data that is not produced or managed by the enterprise.

56. Analyze incident and event data over time, places and individuals.

57. Make life easier for you, and What else do you do to make this work better for you.

58. Define the difference between Big Data and analytics.

59. Standardize the data from different connected systems.

60. Expect your organization to increase its use of Shared Services/COEs.

61. Assure data isolation in a multi tenant environment.

62. Plan to use what you get back from the video from the back end.

63. Build in the foresight for changes that you do not have today.

64. Expect your organization to increase the use of Outsourcing.

65. Leverage your loyalty program in driving your customer strategy.

66. Ensure Access and Equity in the STEM and Digital Skills Workforce.

67. Get involved in a project with other people.

68. Actually build an enterprise wide Center of Excellence.

69. Evaluate the effectiveness of your organizations pay and rewards strategy and practices.

70. Use the process called root cause analysis.

71. Identify applications that can be outsourced to reduce expenses and meet your organizational goals for sustainability.

72. Extend the ways in which you assess the influence of teaching and learning centers.

73. Prefer project status (cost, schedule, issues) and frequency of the same to be communicated.

74. Evaluate the effectiveness of your organizations retention strategy and practices.

75. Build an ecosystem of partners and drive value from them.

76. Identify and evaluate the right partners to help you.

77. Balance the need for efficiency and exploration with fairness and sensitivity to users.

78. Control OS level access to your EC2 instances.

79. Guide your organization that uses mostly Waterfall methodology to Agile.

80. Foster innovation while balancing risk and cost.

81. Attract and retain talent in your Shared Services organization.

82. Strengthen your standards addressing quality control.

83. Accelerate migration and unlock benefit and value early.

84. Leverage existing IT technology investments supporting BI applications.

85. Streamline this process to maximize your returns.

86. Make government perform better and deliver on your key objectives.

87. Get value out of your local compliance processes.

88. Assess your existing applications against cloud migration.

89. Integrate the Public Cloud while still retaining control of your data.

90. Use Programs of Excellence: A Tool for Self Review and Identification.

91. Implement a corporate BYOD program without compromising your enterprise security.

92. Develop and test applications in the cloud.

93. Ensure continuity as you move from concept to engineering to procurement to construction to turn over.

94. Communication project objectives to your teams.

95. Monitor and control activity to ensure performance.

96. Predict, prioritize and capture the value of AI.

97. Know that the investment you are making in analytics is worth it.

98. Know that the investment you m making in analytics is worth it.

99. Know your CoE is delivering value and is heading in the right direction.

100. Dynamically modify it in real time or in a timely way.

101. Become your organization capable of achieving your vision.

102. Decide whether your organization should invest in it.

103. Expect to improve total cost of ownership with the chosen solution.

104. Change that, and remain nimble regardless of your organization size.

105. Prepare the public to make informed choices.

106. Demonstrate to your customers or stakeholders that you met or exceeded the contracted requirements (SLAs).

107. Do you see the return on investment with an analytics strategy.

108. Prioritize goals and know that a particular goal is worthy to pursue.

109. Mitigate the risk of stakeholder rejection.

110. Facilitate adoption of the Performance CoE concept.

111. Define and implement mobile applications end-to-end security.

112. Lead your organization through the change.

113. Interpret regulations: through science or organization rules.

114. Squeeze out more performance, safety, lifetime, and value from batteries.

115. Leverage professional partnerships to enhance the learning experience.

116. Measure the effectiveness of a Cloud Operating Model.

117. Measure the effectiveness of your Cloud Operating Model.

118. Rate the performance of the overall management.

119. Make sure you do not just replicate existing IT problems in your cloud environment.

120. Know your approach to analytics is paying off.

121. Measure the success of a Power BI implementation.

122. Measure trend in customer loyalty over a period of time.

123. Build a foundation that meets your current and future needs.

124. Work collaboratively to promote learning and improvement.

125. Hire today for a diminished workforce 10 years out.

126. Strengthen your standards addressing group audits.

127. Organize to support such competing goals.

128. Prevent and detect unauthorized access to data.

129. Identify, mitigate against and manage risks to your organization.

130. Successfully adopt a Cloud Operating Model.

131. Control costs through predictable resource allocation.

132. Are assemble customer journeys in new and creative ways.

133. Reskill the Engineering and Advanced Manufacturing Workforce for the Digital Economy.

134. Build Talent Pathways through Industry Recognized Credentials.

135. Design public health strategies that address such influences.

136. Measure the impact of productivity (in person days).

137. Project a financial plan when you cannot measure hours or unit costs.

138. Know that your models and algorithms are doing the right thing.

139. Understand clients sequential regimen progression across the CODE network.

140. Ensure you set up your AWS account securely.

141. Run IT as a service, not just cross departmentally throughout your organization, and across multiple organizations and even organizations outside your system.

142. Stay in control of a complex intelligent system.

143. Deal with the continuous pressure to reduce the cost of IT.

144. Pick the right one to deliver the greatest impact for your business, as applied over your data.

145. Show a return on this kind of investment sooner rather than later.

146. Get better insights to increase velocity and close rate on your pipeline.

147. Acquire an understanding of the physics of the system.

148. Use The Six Pillars to create a competitively differentiated experience.

149. Predict the probability of success or failure of new initiatives.

150. Turn this feature off if you do not want it.

151. Position your organizations to embrace such a future.

152. Show ways to increase revenue per employee.

153. Estimate the cost of a large transition like this.

154. Communicate status (frequency, level of detail) to you customers.

155. Prevent disruptions to your organizations daily operation.

156. Communicate with your organizations Directors.

157. Ensure that community members can focus on participating in IT acquisition.

158. Approach the challenges of dealing with a potentially unmanageable amount of data.

159. Get leadership visibly and meaningfully behind the journey to the cloud.

160. Accelerate the adoption of analytics by end users.

161. Use body worn cameras to increase trust between law enforcement and the public.

162. Define the centers role and responsibilities.

163. Ensure your analytics operations are secure.

164. Use costing and budgeting for short term decision making.

165. Know when to do a desk review with a closure note versus a full onsite investigation.

166. Prioritize inclusion as you build your technical teams.

167. Know when things are good enough (the point of diminishing returns).

168. Encourage employees to adopt digital initiatives.

169. Reset your password or set up your UTD account.

170. Hold managers accountable for achieving goals.

171. Satisfy the most immediate needs while you build your capabilities.

172. Decide when to release a video that may contain sensitive footage.

173. Implement this in a highly available and cost efficient way.

174. Implement a strategic, cost effective BI infrastructure.

175. Best enable Distributed Mission Command.

176. Align your employment and training strategy with priorities.

177. Decide which Center of Excellence to use.

178. Fund a Center of Excellence and Innovation.

179. Measure the success of your cultural transformation.

180. Focus your resources on your most valuable data.

181. Manage IT resources in a just-in-time model.

182. Choose a discovery tool for your environment.

183. Plan to increase automation capabilities in the future.

184. Deal with problems that arise when you are working in groups.

185. Select the best environment for net new workloads.

186. Get the most out of your various types of channel partners.

187. Plan for staffing levels in relation to contact volumes.

188. Accelerate innovation efforts in the digital age.

189. Expect to achieve organizational excellence with such disappointing numbers.

190. Get results fast without sacrificing quality.

191. Most effectively reach as many of them as possible.

192. Assess the full range of outcomes for your potential investments.

193. And/or your department contribute to this initiative.

194. Tackle the challenges of AI responsibility, ethics and governance.

195. Build and maintain trust in an increasingly transparent market.

196. Ensure you deploy Azure in line with best practices.

197. Manage politics and culture within your organization.

198. Want to show up in front of your customers.

199. Effectively train staff in such new skills.

200. Develop strategic relationships with your vendors, partners and independent developers.

201. Best utilize the functions or centers of excellence.

202. Develop capabilities to capitalize on such trends at scale.

203. Decide your target audience for promotions.

204. Best target the highest risk, most vulnerable workers.

205. Read selected lessons without opening each lesson.

206. Find out if there are deviations from plans during execution.

207. Mobilize your digital vision across your organization.

208. Develop and integrate your first mobile App.

209. Maintain and enhance/increase the CI talent pool.

210. Wish to receive notification of the correction.

211. Deliver the right intervention to prevent crime.

212. Organize to fight turnover and maximize results.

213. Facilitate this with the least amount of effort.

214. Enable an efficient transformation function.

215. Determine which incidents require a lessons learned report.

216. Coordinate all of the moving parts of a new implementation.

217. Be sure that only those who are legitimately sick receive treatment.

218. Create e infrastructures that overcome fragmentation.

219. Create the right conditions for alignment.

220. Maximize the ROI of incentive compensation.

221. Know each facility maintains its quality.

222. Model various operational scenarios and potential outcomes.

223. Manage different versions of the plans/budgets during the process.

224. Address over or under allocation variances.

225. Enhance collaborations across large facilities and CI projects.

226. Transparently intercept mobile requests and redirect them to the cloud.

227. Ensure questions or requests are quickly and correctly addressed.

228. Manage dimensions across ERPs and other systems.

229. Ensure that you migrate workloads correctly and quickly to the cloud.

230. Distinguish between what is actually good from what only seems to be good.

231. Support newcomers social emotional needs.

232. Order the required Pathway application manual for your organization.

233. Propose that you then consolidate that information.

234. Deal with the plethora of potential projects.

235. Address deviations from those guidelines.

236. Build a CI community: what are the impediments and opportunities.

To visualize the Center of Excellence work and manage it, I have built a Center of Excellence Kanban board that is broken down into 1142 Work Items that are prioritized into their Workflows. It’s for where to get started on your current or impending Center of Excellence journey.

How many tasks can you move to Done?

Check it out here: https://theartofservice.com/Center-of-Excellence-Kanban

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khalilhumam · 5 years ago

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The Brookings glossary of AI and emerging technologies

New Post has been published on http://khalilhumam.com/the-brookings-glossary-of-ai-and-emerging-technologies/

The Brookings glossary of AI and emerging technologies

By John R. Allen, Darrell M. West

Algorithms:

According to author Pedro Domingos, algorithms are “a sequence of instructions telling a computer what to do.”[1] These software-based coding rules started with simple and routine tasks, but now have advanced into more complex formulations, such as providing driving instructions for autonomous vehicles, identifying possible malignancies in X-rays and CT scans, and assigning students to public schools. Algorithms are widely used in finance, retail, communications, national defense, and many other areas.

Artificial Intelligence (AI):

Indian engineers Shukla Shubhendu and Jaiswal Vijay define AI as “machines that respond to stimulation consistent with traditional responses from humans, given the human capacity for contemplation, judgment, and intention.”[2] This definition emphasizes several qualities that separate AI from mechanical devices or traditional computer software, specifically intentionality, intelligence, and adaptability. AI-based computer systems can learn from data, text, or images and make intentional and intelligent decisions based on that analysis.

Augmented Reality (AR):

Augmented reality puts people in realistic situations that are augmented by computer-generated video, audio, or sensory information. This kind of system allows people to interact with actual and artificial features, be monitored for their reactions, or be trained on the best ways to deal with various stimuli.

Big Data:

Extremely large data sets that are statistically analyzed to gain detailed insights. The data can involve billions of records and require substantial computer-processing power. Data sets are sometimes linked together to see how patterns in one domain affect other areas. Data can be structured into fixed fields or unstructured as free-flowing information. The analysis of big data sets can reveal patterns, trends, or underlying relationships that were not previously apparent to researchers.

Chatbots:

Automated tools for answering human questions. Chatbots are being used in retail, finance, government agencies, nonprofits, and other organizations to respond to frequently asked questions or routine inquiries.

Cloud Computing:

Data storage and processing used to take place on personal computers or local servers controlled by individual users. In recent years, however, storage and processing have migrated to digital servers hosted at data centers operated by internet platforms, and people can store information and process data without being in close proximity to the data center. Cloud computing offers convenience, reliability, and the ability to scale applications quickly.

Computer Vision (CV):

Computers that develop knowledge based on digital pictures or videos.[3] For example, cameras in automated retail outlets that are connected to CV systems can observe what products shoppers picked up, identify the specific items and their prices, and charge consumers’ credit card or mobile payment system without involving a cash register or sales clerk. CV also is being deployed to analyze satellite images, human faces, and video imagery.

Connected Vehicles:

Cars, trucks, and buses that communicate directly with one another and with highway infrastructure. This capacity speeds navigation, raises human safety, and takes advantage of the experiences of other vehicles on the road to improve the driving experience.

Data Analytics:

The analysis of data to gather substantive insights. Researchers use statistical techniques to find trends or patterns in the data, which give them a better understanding of a range of different topics. Data analytic approaches are used in many businesses and organizations to track day-to-day activities and improve operational efficiency.

Data Mining:

Techniques that analyze large amounts of information to gain insights, spot trends, or uncover substantive patterns. These approaches are used to help businesses and organizations improve their processes or identify associations that shed light on relevant questions.

Deepfakes:

Digital images and audio that are artificially altered or manipulated by AI and/or deep learning to make someone do or say something he or she did not actually do or say. Pictures or videos can be edited to put someone in a compromising position or to have someone make a controversial statement, even though the person did not actually do or say what is shown. Increasingly, it is becoming difficult to distinguish artificially manufactured material from actual videos and images.

Deep Learning:

A subset of machine learning that relies on neural networks with many layers of neurons. In so doing, deep learning employs statistics to spot underlying trends or data patterns and applies that knowledge to other layers of analysis. Some have labeled this as a way to “learn by example” and a technique that “perform[s] classification tasks directly from images, text, or sound” and then applies that knowledge independently.[4] Deep learning requires extensive computing power and labeled data, and is used in medical research, automated vehicles, electronics, and manufacturing, among other areas.

Digital Sovereigns:

The speed, scope, and timing of technology innovation today is often decided not by government officials but by coders, software designers, and corporate executives. Digital sovereigns set the rules of the road and terms of service for consumers. What they decide, directly or indirectly, has far-reaching consequences for those using their software or platform. The power of business decisionmakers raises important governance questions regarding who should decide on matters affecting society as a whole and the role that policymakers, consumers, and ethicists should play in digital innovation.

Distributed Collaboration:

Connecting frontline people with others who have differing skills and getting them to work together to solve problems. Distributed collaboration differs from current governance paradigms that emphasize hierarchical, top-down decisionmaking by those who do not always have relevant knowledge about the issues being addressed. The new model takes advantage of the fact that a range of skills are needed to resolve technology issues, and those skills are located in different subject areas and organizational parts. Rather than keeping AI expertise in isolation, distributed collaboration brings together software and product designers, engineers, ethicists, social scientists, and policymakers to draw on their respective expertise and integrate their knowledge to solve pressing problems.

Dual-Use Technologies:

Many technologies can be used in a good or ill manner. The very same facial recognition system could be used to find missing children or provide a means for mass surveillance. It is not the technology per se that raises ethical issues but how the technology is put to use. The dual-use nature of technologies makes regulation difficult because it raises the question of how to gain the benefits of technology innovation while avoiding its detrimental features.

Facial Recognition (FR):

A technology for identifying specific people based on pictures or videos. It operates by analyzing features such as the structure of the face, the distance between the eyes, and the angles between a person’s eyes, nose, and mouth. It is controversial because of worries about privacy invasion, malicious applications, or abuse by government or corporate entities. In addition, there have been well-documented biases by race and gender with some facial recognition algorithms.

5G Networks:

These are fifth-generation wireless telecommunications networks that have been deployed in major cities and feature faster speeds and enhanced capabilities for transmitting data and images. As such, 5G networks enable new digital products and services, such as video streaming, autonomous vehicles, and automated factories and homes that require a fast broadband.

Hyperwar:

High-tech military situations in which robots, sensors, AI, and autonomous systems play important roles and command decisions have to unfold at speeds heretofore unseen in warfare. Because of the acceleration of the pace and scope of conflict, countries will have to conduct simultaneous operations in every warfare domain and national leaders will need to accelerate technology innovation to build a safe and stable future.[5]

Machine Learning (ML):

According to Dorian Pyle and Cristina San Jose of the McKinsey Quarterly, machine learning is “based on algorithms that can learn from data without relying on rules-based programming.”[6] ML represents a way to classify data, pictures, text, or objects without detailed instruction and to learn in the process so that new pictures or objects can be accurately identified based on that learned information. ML furthermore can be used to estimate continuous variables (such as estimating home sales prices) or to play games. Many of its insights come by examining prior data and learning how to improve understanding.

Natural Language Processing (NLP):

The analysis of textual information to make sense of its meaning and intentions. NLP software can take a large amount of text and see how words are linked together to assess positive or negative sentiment, relationships, associations, and meaning. For example, researchers can study medical records to see which patient symptoms appear to be most related to particular illnesses.

Neural Networks:

Researchers use computer software to “perform some task by analyzing training examples” and by grouping data based on common similarities.[7] Similar to the neural nodes of a brain, neural networks learn in layers and build complex concepts out of simpler ones. They break up tasks, identify objects at a number of different levels, and apply that knowledge to other activities. These kinds of systems allow computers to learn and adapt to changing circumstances, similar to the way a brain functions. Deep learning and many of the most prominent recent applications of machine learning operate through neural networks (e.g., driverless cars, deepfakes, and AlphaGo game playing).

Quantum Computing:

Quantum computers have tremendous capacity for storing and processing information because their storage processes are not in the form of a zero or one, as is the case with traditional computers. Rather, they take advantage of superposition—the fact that electrons can be in two places at once—to create “quantum bits” that store multiple values in each point.[8] That capability dramatically increases storage capacity and decreases processing times, thereby improving the scope of data, textual, or image analysis.

Singularity:

Futurist Ray Kurzweil describes a singularity as a “machine-based superintelligence [that is] greater than human intelligence.”[9] It combines advanced computing power with artificial intelligence, machine learning, and data analytics to create super-powered entities. There are extensive (and unresolved) debates regarding whether humanity will face a computing singularity in the next 50, 100, or 250 years.

Social Credit Systems:

The ubiquity of people’s online activities enables technology that tracks behavior and rates people based on their online actions. As an illustration, some organizations have piloted systems that compile data on social media activities, personal infractions, and behaviors such as paying taxes on time. They use that data to rate people for creditworthiness, travel, school enrollment, and government positions.[10] These systems are problematic from an ethical standpoint because they lack transparency and can be used to penalize political opponents.

Supervised Learning:

According to Science magazine, supervised learning is “a type of machine learning in which the algorithm compares its outputs with the correct outputs during training. In unsupervised learning, the algorithm merely looks for patterns in a set of data.”[11] Supervised learning allows ML and AI to improve information processing and become more accurate.

Techlash:

The backlash against emerging technologies that has developed among many individuals. People worry about a host of problems related to technology innovation, such as privacy invasions, mass surveillance, widening income inequality, and possible job losses. Figuring out how to assuage understandable human fears is a major societal challenge going forward.

Virtual Reality (VR):

Virtual reality uses headsets equipped with projection visors to put people in realistic-seeming situations that are completely generated by computers. People can see, hear, and experience many types of environments and interact with them. By simulating actual settings, VR can train people how to deal with various situations, vary the features that are observed, and monitor how people respond to differing stimuli.

[1] Pedro Domingos, The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World (New York: Basic Books, 2018). [2] Shukla Shubhendu and Jaiswal Vijay, “Applicability of Artificial Intelligence in Different Fields of Life,” International Journal of Scientific Engineering and Research, vol. 1, no. 1 (September 2013), pp. 28–35. [3] Jason Brownlee, “A Gentle Introduction to Computer Vision,” Machine Learning Mastery, July 5, 2019. [4] Math Works, “What Is Deep Learning?” undated. [5] John R. Allen and Amir Husain, “Hyperwar and Shifts in Global Power in the AI Century,” in Amir Husain and others, Hyperwar: Conflict and Competition in the AI Century (Austin, TX: SparkCognition Press, 2018), p. 15. [6] Dorian Pyle and Cristina San Jose, “An Executive’s Guide to Machine Learning,” McKinsey Quarterly, June, 2015. [7] Larry Hardesty, “Explained: Neural Networks,” MIT News, April 14, 2017. [8] Cade Metz, “In Quantum Computing Race, Yale Professors Battle Tech Giants,” New York Times, November 14, 2017, p. B3. [9] Quoted in Tom Wheeler, From Gutenberg to Google: The History of Our Future (Brookings, 2019), p. 226. Also see Ray Kurzweil, The Singularity Is Near: Where Humans Transcend Biology (London: Penguin Books, 2006). [10] Jack Karsten and Darrell M. West, “China’s Social Credit System Spreads to More Daily Transactions,” TechTank (blog), Brookings, June 18, 2018. [11] Matthew Hutson, “AI Glossary: Artificial Intelligence, in So Many Words,” Science, July 7, 2017.

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nicaurybenitezcortorreal-blog · 8 years ago

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Introducing Microsoft Machine Learning Server 9.2 Release

This post is authored by Nagesh Pabbisetty, Partner Director of Program Management at Microsoft.

Earlier this year, Microsoft CEO Satya Nadella shared his vision for Microsoft and AI, pointing to Microsoft’s beginnings as a tools company, and our current focus on democratizing AI by putting tools “in the hands of every developer, every organization, every public sector organization around the world”, so that they can build their own intelligence and AI capabilities.

Today, we are taking a significant step in realizing Satya’s vision by launching Microsoft Machine Learning Server 9.2, our most comprehensive machine learning and advanced analytics platform for enterprises. We have exciting updates to share, including full data science lifecycle support (data preparation, modeling and operationalization) for Python as a peer to R, and a repertoire of high performance distributed ML and advanced analytics algorithm packages.

We started the journey of transforming Microsoft R Server into Machine Learning Server a year ago, by delivering innovations in Microsoft R Server 9.0 and 9.1. We made significant enhancements in this release to create the Machine Learning Server 9.2 platform which replaces Microsoft R Server and offers powerful ML capabilities.

Microsoft Machine Learning Server is the most inclusive enterprise platform that caters to the needs of all constituents – data engineers, data scientists, line-of-business programmers and IT professionals – with full support for Python and R. This flexible platform offers a choice of languages and features algorithmic innovation that brings the best of open source and proprietary worlds together. It enables best-in-class operationalization support for batch and real-time.

Microsoft Machine Learning Server includes:

High-performance ML and AI wherever your data lives.

The best AI innovation from Microsoft and open source.

Simple, secure and high-scale operationalization and administration.

A collaborative data science environment for intelligent application development.

Deep ecosystem engagements, to deliver customer success with optimal TCO.

It’s now easier than ever to procure and use Microsoft Machine Learning Server on all platforms. Licensing has been simplified to the following, effective October 1st 2017:

Microsoft Machine Learning Server is built into SQL Server 2017 at no additional charge.

Microsoft Machine Learning Server stand-alone for Linux or Windows is licensed core-for-core as SQL Server 2017.

All customers who have purchased Software Assurance for SQL Server Enterprise Edition are entitled to use 5 nodes of Microsoft Machine Learning Server for Hadoop/Spark for each core of SQL Server 2017 Enterprise Edition under SA. In addition, we are removing the core limit per-node; customers can have unlimited cores per node of Machine Learning Server for Hadoop/Spark.

You can immediately download Microsoft Machine Learning Server 9.2 from MSDN and Visual Studio Dev Essentials. It comes packed with the power of the open source R and Python engines, making both R and Python ready for enterprise-class ML and advanced analytics. Also check out the R Client for Windows, R Client for Linux, and Visual Studio 2017 with R and Python Tools.

Let’s take a peek at each of the key areas of the new Microsoft Machine Learning Server outlined above.

1. High-performance Machine Learning and AI, Wherever Data Lives

The volume of the data that’s being used by enterprises to make smart business decisions is growing exponentially. The traditional paradigm requires users to move data to compute which introduces challenges with latency, governance and cost, even if it was possible to move the data to where compute is. The modern paradigm is to take compute to where the data is, to unlock intelligence, and this is Microsoft’s approach.

In enterprises, it is common to have data spread across multiple data platforms and migrate data from one platform to another, over time. In such a world, it is essential that ML and analytics are available on multiple platforms, and are portable, and Microsoft delivers on this need. Microsoft Machine Learning Server 9.2 runs on Windows, three flavors of Linux, the most popular distributions of Hadoop Spark and in the latest release of SQL Server 2017. As always, we will soon make this release available on Azure as Machine Learning Server VMs, SQL Server VMs, and as Machine Learning Services on Azure HDInsight, in addition to an ever-growing portfolio of cloud services.

Today, we are also announcing Public Preview of R Services on Azure SQL DB, to make it easy for customers who are going cloud-first or transitioning to the cloud from on-premises.

For more information, review the links below:

2. The Best AI Innovation from Microsoft and Open Source

As make AI accessible to every individual and organization, one of our key goals is to use this technology to amplify human ingenuity through intelligent technology. We are designing AI innovations that extend and empower human capabilities in all aspects of life.  We are infusing AI across our most popular products and services, and creating new ways to interact more naturally with technology. Offerings such as the Microsoft Cognitive Toolkit for deep learning, our Cognitive Services collection of intelligent APIs, SQL Server Machine Learning Services and Azure Machine Learning exemplify our approach.

Microsoft Machine Learning Server includes a rich set of highly scalable and distributed set of algorithms such as revoscaler, , and microsoftML that can work on data sizes larger than the size of physical memory, and run on a wide variety of platforms in a distributed manner.

The open source ecosystem is innovating at a fast pace as well, with AI toolkits such as TensorFlow, MXNet, Keras and Caffe, in addition to our open source Cognitive Toolkit.

Microsoft Machine Learning Server 9.2 bridges these two worlds, enabling enterprises to build on a single ML platform where one can bring any R or Python open source ML package, and have it work side-by-side with any proprietary innovation from Microsoft. This is a key investment area for us. You can learn more from the resources below:

3. Simple, Secure and High-Scale Operationalization and Administration

Enterprises that rely on traditional paradigms and environments for operationalization end up investing a lot of time and effort towards this area. It is not uncommon for data scientists to complete their models and hand them over to line-of-business programmers to translate that into popular LOB languages and APIs. The translation time for the model, iterations to keep it valid and current, regulatory approval, managing permissions through operationalization – all of these things are big pain points, and they result in inflated costs and delays.

Microsoft Machine Learning Server offers the best-in-class operationalization solution in the industry. From the time an ML model is completed, it takes just a few clicks to generate web services APIs that can be hosted on a server grid (either on premises or in the cloud) which can then be integrated with LOB applications easily. In addition, Microsoft Machine Learning Server integrates seamlessly with Active Directory and Azure Active Directory, and includes role-based access control to make sure that the security and compliance needs of the enterprise are satisfied. The ability to deploy to an elastic grid lets you scale seamlessly with the needs of your business, both for batch and real-time scoring.

For more information, refer to the links below:

Machine Learning Server operationalization video and deck.

, batch scoring.

4. A Collaborative Data Science Environment for Intelligent Application Development

In enterprises, different departments take the lead for different aspects of the data science life-cycle. For instance, data engineers lead data preparation, data scientists lead experimentation and model building, IT professionals lead deployment and operationalization, and LOB programmers develop and enhance applications with intelligence, tailoring them to the needs of the business. With the in-database analytics capability of SQL Server 2017 and SQL Server 2016 (powered by Microsoft Machine Learning Services), all these constituents can work collaboratively and in the context of the leading mission critical database that is trusted by enterprises all over the world.

Python and R are the most popular languages for ML and advanced analytics. The choice of a language depends on the expertise and culture of engineers and scientists, the data science problems to be solved, and the availability of algorithms toolkits for the chosen language. Each language is supported by a choice of open-source IDEs. It’s not unusual to have debates on which language to choose because enterprises think they have to make an either-or choice.

With Microsoft Machine Learning Server, both R and Python are fully supported. You can bring in and use the latest open source toolkits along with the included Microsoft toolkits for AI and advanced analytics, all on top of a single enterprise-grade platform. Specific enhancements to support Python in the current release include:

New Python packages: revoscalepy and microsoftml, bringing high performance and battle tested machine learning algorithms to Python users.

Pre-trained cognitive models for image classification and sentiment analysis.

Interoperability with PySpark.

Python models deployed as web services.

Real-time and batch scoring of Python models.

Concurrent with this release, Microsoft is also releasing a public preview of Azure Machine Learning, a comprehensive environment for data science and AI. We will integrate Microsoft Machine Learning Server capabilities with this platform, to realize an industry-leading workbench for data science and AI.

For more information, refer to the links below:

5. Deep Ecosystem Engagements, to Deliver Customer Success with Optimal TCO

Individuals embarking on the journey of making their applications intelligent, or, simply wanting to learn the new world of AI and ML, need the right learning resources to help them get started. Microsoft provides several learning resources, and has engaged several training partners to create a repertoire of solution templates to help you ramp up and become productive quickly, including the following:

Marketing Campaign Optimization on SQL Server R Server for HDI Spark Cluster.

Predicting Hospital Length of Stay on SQL Server.

Predicting Loan Credit Risk on SQL Server on R Server for HDI Spark Cluster.

Loan Charge-off Prediction on SQL Server on R Server for HDI Spark Cluster.

Fraud Detection for Online Retailers on SQL Server on R Server for HDI Spark Cluster.

Enterprises have big investments in infrastructure and applications and may need the help of partners such as Systems Integrators (SIs) and Independent Software Vendors (ISVs) to help them transform into the world of intelligent applications. Microsoft has nurtured a vibrant ecosystem of partners to help our customers here. Learn about some of our strategic partnerships at the links below:

Summary

With the launch of Microsoft Machine Learning Server 9.2, we are proud to bring enterprises worldwide an inclusive platform for machine learning and advanced analytics. We have created a better-together environment that brings intelligence where the data lives, supports both R and Python, both open source and proprietary innovation, the ability to work on the data science lifecycle across a wide variety of platforms, and infuse intelligence at scale, both in batch and real-time contexts, with APIs for the most popular LOB languages.

Adopting machine learning and advanced analytics requires a holistic approach that transcends technology, people and processes. We are proud to continue delivering the best tools, platforms and ecosystem to ensure that enterprise users are set up for success. Our next steps are to integrate Azure Machine Learning and Microsoft Machine Learning Server closely, and continue to take machine learning to our customers’ data, wherever it may reside.

Nagesh

Source

https://blogs.technet.microsoft.com/machinelearning/2017/09/25/introducing-microsoft-machine-learning-server-9-2-release/

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