Enterprise AI in the Cloud
A Practical Guide to Deploying End-to-End Machine Learning and ChatGPT Solutions
Tech Today Series

Embrace emerging AI trends and integrate your operations with cutting-edge solutions

Enterprise AI in the Cloud: A Practical Guide to Deploying End-to-End Machine Learning and ChatGPT Solutions is an indispensable resource for professionals and companies who want to bring new AI technologies like generative AI, ChatGPT, and machine learning (ML) into their suite of cloud-based solutions. If you want to set up AI platforms in the cloud quickly and confidently and drive your business forward with the power of AI, this book is the ultimate go-to guide. The author shows you how to start an enterprise-wide AI transformation effort, taking you all the way through to implementation, with clearly defined processes, numerous examples, and hands-on exercises. You'll also discover best practices on optimizing cloud infrastructure for scalability and automation.

Enterprise AI in the Cloud helps you gain a solid understanding of:

• AI-First Strategy: Adopt a comprehensive approach to implementing corporate AI systems in the cloud and at scale, using an AI-First strategy to drive innovation
• State-of-the-Art Use Cases: Learn from emerging AI/ML use cases, such as ChatGPT, VR/AR, blockchain, metaverse, hyper-automation, generative AI, transformer models, Keras, TensorFlow in the cloud, and quantum machine learning
• Platform Scalability and MLOps (ML Operations): Select the ideal cloud platform and adopt best practices on optimizing cloud infrastructure for scalability and automation
• AWS, Azure, Google ML: Understand the machine learning lifecycle, from framing problems to deploying models and beyond, leveraging the full power of Azure, AWS, and Google Cloud platforms
• AI-Driven Innovation Excellence: Get practical advice on identifying potential use cases, developing a winning AI strategy and portfolio, and driving an innovation culture
• Ethical and Trustworthy AI Mastery: Implement Responsible AI by avoiding common risks while maintaining transparency and ethics
• Scaling AI Enterprise-Wide: Scale your AI implementation using Strategic Change Management, AI Maturity Models, AI Center of Excellence, and AI Operating Model

Whether you're a beginner or an experienced AI or MLOps engineer, business or technology leader, or an AI student or enthusiast, this comprehensive resource empowers you to confidently build and use AI models in production, bridging the gap between proof-of-concept projects and real-world AI deployments.

With over 300 review questions, 50 hands-on exercises, templates, and hundreds of best practice tips to guide you through every step of the way, this book is a must-read for anyone seeking to accelerate AI transformation across their enterprise.

Introduction xvii

Part I: Introduction

Chapter 1: Enterprise Transformation with AI in the Cloud 3

Chapter 2: Case Studies of Enterprise AI in the Cloud 19

Part II: Strategizing and Assessing for Ai

Chapter 3: Addressing the Challenges with Enterprise AI 31

Chapter 4: Designing AI Systems Responsibly 41

Chapter 5: Envisioning and Aligning Your AI Strategy 50

Chapter 6: Developing An AI Strategy and Portfolio 57

Chapter 7: Managing Strategic Change 66

Part III: Planning and Launching a Pilot Project

Chapter 8: Identifying Use Cases for Your AI/ml Project 79

Chapter 9: Evaluating AI/ml Platforms and Services 106

Chapter 10: Launching Your Pilot Project 152

Part IV: Building and Governing Your Team

Chapter 11: Empowering Your People Through Org Change Management 163

Chapter 12: Building Your Team 173

Part V: Setting Up Infrastructure and Managing Operations

Chapter 13: Setting Up An Enterprise AI Cloud Platform Infrastructure 187

Chapter 14: Operating Your AI Platform with Mlops Best Practices 217

Part VI: Processing Data and Modeling

Chapter 15: Process Data and Engineer Features in The Cloud 243

Chapter 16: Choosing Your AI/ml Algorithms 268

Chapter 17: Training, Tuning, and Evaluating Models 315

Part VII: Deploying and Monitoring Models

Chapter 18: Deploying Your Models Into Production 345

Chapter 19: Monitoring Models 361

Chapter 20: Governing Models for Bias and Ethics 377

Part VIII: Scaling and Transforming AI

Chapter 21: Using the AI Maturity Framework to Transform Your Business 391

Chapter 22: Setting Up Your AI Coe 407

Chapter 23: Building Your AI Operating Model and Transformation Plan 416

Part IX: Evolving and Maturing AI

Chapter 24: Implementing Generative AI Use Cases With Chatgpt for the Enterprise 433

Chapter 25: Planning for the Future of AI 465

Chapter 26: Continuing Your AI Journey 479

Index 485

RABI JAY is a recognized IT expert with over 15 years of experience working in roles such as VP of Architecture, Digital Platform Strategy Lead, and Global Alliance Manager at Deloitte Consulting, as well as at HCL America and SapientRazorfish. He has been instrumental in driving large-scale, enterprise-level Cloud and AI transformations across diverse industries like retail, telecom, finance, and tech. He holds sought-after certifications in AWS Machine Learning, AWS Solutions Architect, and Microsoft Azure. You can connect with Rabi through his authoritative LinkedIn newsletter, Enterprise AI Transformation.