Mastering AI Platforms: From Setup to Advanced Operations
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Mastering AI Platforms: From Setup to Advanced Operations

How to design, build and manage AI Platform on Cloud

About the Book

Artificial Intelligence (AI) has emerged as a critical driver of innovation across industries in today's rapidly evolving digital landscape. As organizations increasingly adopt AI technologies to gain a competitive edge, the need for effective AI platforms has become more crucial than ever. "Mastering AI Platforms: From Setup to Advanced Operations" is a comprehensive guide to understanding, building, and managing AI platforms, from foundational concepts to advanced operations.

This book is designed for business leaders, data scientists, engineers, and AI practitioners looking to implement AI solutions in their organizations. It covers the entire lifecycle of AI platform operations, providing actionable insights and practical strategies for success.

The journey begins with Chapter 1: Introduction to AI Platforms, where readers will explore the fundamentals of AI platforms, the evolution of AI in the enterprise, and key components that form the backbone of successful AI implementations. Through real-world case studies, this chapter highlights the benefits and challenges of AI adoption.

Chapter 2: Setting Up an AI Platform dives into the technical aspects of choosing and configuring the right AI platform, discussing hardware, software, and data infrastructure. This chapter also covers essential considerations for integrating AI with existing systems and ensuring security during the initial deployment.

Data is the lifeblood of AI, and Chapter 3: Data Management for AI provides a deep dive into the critical processes of data collection, storage, and governance. Readers will gain insights into managing big data, ensuring data quality, and maintaining privacy and compliance standards.

Building AI models is at the heart of AI development, and Chapter 4: Building AI Models offers a step-by-step approach to model design, training, and evaluation. The chapter also explores advanced techniques like transfer learning and handling imbalanced data.

Once the models are built, deploying them effectively is key to realizing AI's full potential. Chapter 5: Deploying AI Models examines deployment strategies, processing methods, and scaling considerations, along with the use of containerization technologies like Docker and Kubernetes.

In Chapter 6: AI in Production, readers will learn how to manage AI workflows, optimize performance, and handle errors in production environments. This chapter emphasizes the importance of maintaining model interpretability and continuously improving AI systems based on user feedback.

Advanced AI operations are covered in Chapter 7: Advanced AI Operations, where the focus shifts to MLOps, AutoML, and cutting-edge techniques like federated learning and AI for real-time analytics. Industry-specific AI solutions and governance are also discussed in detail.

Generative AI, a transformative technology, is explored in Chapter 8: Generative AI Applications. This chapter provides an overview of generative models, including Natural Language Generation (NLG), Generative Adversarial Networks (GANs), and their applications across various industries, along with the ethical implications of these technologies.

Real-world applications are showcased in Chapter 9: Case Studies and Applications, with in-depth explorations of AI's impact across sectors such as healthcare, finance, retail, manufacturing, and transportation. The chapter also examines emerging trends and future applications of AI.

Chapter 10: AI Platform Tools and Technologies provides a comprehensive overview of major AI platforms, data management tools, and development environments for those looking to get hands-on with AI tools. This chapter also covers deployment, monitoring, and visualization tools, offering a balanced view of open-source versus commercial options.

The book concludes with Chapter 11: Future of AI Platform Operations, which looks ahead at emerging technologies, the role of AI in digital transformation, and the ethical considerations that will shape AI's future. The chapter provides a strategic roadmap for organizations aiming to thrive in an AI-driven world, highlighting the skills and workforce required to navigate the coming disruptions.

Whether you are just beginning your AI journey or looking to scale your AI operations to new heights, "Mastering AI Platforms: From Setup to Advanced Operations" equips you with the knowledge and tools you need to succeed in the rapidly evolving world of AI.

About the Author

Mansura Habiba
Mansura Habiba

Mansura Habiba is the Principal Platform Architect for the AI and HPC Platform at IBM in Ireland, where she leads the development and continuous operation of AI analysis platforms. With over 15 years of experience in the software, infrastructure, and services industries, she specializes in AI-driven solutions and high-performance computing. Mansura holds a Ph.D. in Computer Science from the University of Maynooth, Ireland, where she focused on deep learning and neural networks for time series analysis.

Mansura's work includes optimizing inventory management, automating processes, and improving data quality, which has led to significant operational efficiency gains. Her contributions to the field are recognized through numerous awards, patents, and publications in international journals and conferences.

Mansura is also an accomplished author, with previous works on cloud architecture and operations. In her latest book, she provides a comprehensive guide to AI platforms, covering everything from setup and data management to deploying advanced AI models and exploring future trends in AI operations. She actively shares her knowledge at industry conferences, on social media, and through various publications and speaking engagements.

Mansura also serves as a reviewer and board member for IEEE journals. She has filed twelve patents related to Neural Networks, Workload Migration, Security, Blockchain, Quantum, Edge computing, and Sustainability, with more pending in Generative AI.

Going Forward, she will be focusing on guiding AI adoption across products to solve real-world problems. She prioritizes scalable, agile, and ethical AI solutions, leveraging her expertise in AI architecture and cloud operations to drive impactful innovation

Mansura's motivation for writing this book stems from her deep passion for artificial intelligence and a desire to demystify AI platforms for practitioners at all levels. Throughout her career, she has encountered numerous challenges and breakthroughs in implementing AI solutions, and she realized the importance of sharing these insights to help others navigate the complex AI landscape. By compiling her experiences and knowledge into this book, Mansura aims to provide a practical, comprehensive resource that empowers others to leverage AI technologies effectively and efficiently. Her goal is to inspire innovation and foster a deeper understanding of AI's potential to transform industries and improve lives.

Table of Contents

    • Preface
      • Who This Book Is For
      • What This Book Covers
      • How to Get in Touch
    • Chapter 1:Modernizing AI Platforms - An Introductory Overview
      • Understanding AI Platform
      • Discovering the challenges in AI platform operations
      • Discovering Myths in AI Platform Operations
      • Summary
      • References
    • Chapter 2:The Machine Learning Lifecycle
      • Introduction to Machine Learning and Deep Learning
      • Learning about the critical stages of the Machine Learning Lifecycle
      • Exploring the Building Blocks of the Machine Learning Lifecycle System
      • Understanding the characteristics of the Machine learning life cycle
      • Discovering the functional and non-functional requirements for an AI Platform
      • Best practices and recommendations for solutioning a Machine Learning deployment model
      • Summary
      • References
    • Chapter 3: Setting Up an AI Platform
      • Understanding the difference between traditional workload platforms and AI platform
      • Exploring architecture patterns for AI platform
      • Exploring the critical characteristics of the AI model management platform
      • Understanding the Security Considerations in AI Setup
      • Understanding the AI governance requirement for transparent adoption
      • Summary
      • References
    • Chapter 4:Data Management for AI
      • Understanding Data lifecycle and different data operations
      • Understanding Data Management Platform
      • Exploring the core component of the Data Management Platform
      • Component Model of Data platform
      • Summary
      • References

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