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Table of Contents
COVER
TABLE OF CONTENTS
TTILE PAGE
Introduction
HOW THIS BOOK IS ORGANIZED WHO SHOULD READ THIS BOOK? WHY YOU SHOULD READ THIS BOOK UNIQUE FEATURES
PART I: Introduction
1 Enterprise Transformation with AI in the Cloud UNDERSTANDING ENTERPRISE AI TRANSFORMATION
LEVERAGING ENTERPRISE AI OPPORTUNITIES
WORKBOOK TEMPLATE - ENTERPRISE AI TRANSFORMATION CHECKLIST
SUMMARY
REVIEW QUESTIONS
ANSWER KEY
2 Case Studies of Enterprise AI in the Cloud
CASE STUDY 1: THE U.S. GOVERNMENT AND THE POWER OF HUMANS AND MACHINES WORKING TOGETHER TO SOLVE PROBLEMS AT SCALE
CASE STUDY 2: CAPITAL ONE AND HOW IT BECAME A LEADING TECHNOLOGY ORGANIZATION IN A HIGHLY REGULATED ENVIRONMENT
CASE STUDY 3: NETFLIX AND THE PATH COMPANIES TAKE TO BECOME WORLD-CLASS
WORKBOOK TEMPLATE - AI CASE STUDY
SUMMARY
REVIEW QUESTIONS
ANSWER KEY
PART II: Strategizing and Assessing for AI
3 Addressing the Challenges with Enterprise AI
CHALLENGES FACED BY COMPANIES
IMPLEMENTING ENTERPRISE-WIDE AI
HOW DIGITAL NATIVES TACKLE AI ADOPTION
GET READY: AI TRANSFORMATION IS MORE CHALLENGING THAN DIGITAL TRANSFORMATION
CHOOSING BETWEEN SMALLER PoC POINT
SOLUTIONS AND LARGE-SCALE AI INITIATIVES
WORKBOOK TEMPLATE: AI CHALLENGES ASSESSMENT
SUMMARY
REVIEW QUESTIONS
ANSWER KEY
4 Designing AI Systems Responsibly THE PILLARS OF RESPONSIBLE AI
WORKBOOK TEMPLATE: RESPONSIBLE AI DESIGN
TEMPLATE
SUMMARY
REVIEW QUESTIONS
ANSWER KEY
5 Envisioning and Aligning Your AI Strategy
STEP-BY-STEP METHODOLOGY FOR ENTERPRISEWIDE AI
WORKBOOK TEMPLATE: VISION ALIGNMENT
WORKSHEET
SUMMARY
REVIEW QUESTIONS
ANSWER KEY
6 Developing an AI Strategy and Portfolio
LEVERAGING YOUR ORGANIZATIONAL CAPABILITIES FOR COMPETITIVE ADVANTAGE
INITIATING YOUR STRATEGY AND PLAN TO KICKSTART ENTERPRISE AI
WORKBOOK TEMPLATE: BUSINESS CASE AND AI STRATEGY
SUMMARY
REVIEW QUESTIONS
ANSWER KEY
7 Managing Strategic Change
ACCELERATING YOUR AI ADOPTION WITH STRATEGIC CHANGE MANAGEMENT
WORKBOOK TEMPLATE: STRATEGIC CHANGE MANAGEMENT PLAN
SUMMARY
REVIEW QUESTIONS
ANSWER KEY
PART III: Planning and Launching a Pilot Project
8 Identifying Use Cases for Your AI/ML Project
THE USE CASE IDENTIFICATION PROCESS FLOW
PRIORITIZING YOUR USE CASES
USE CASES TO CHOOSE FROM
WORKBOOK TEMPLATE: USE CASE IDENTIFICATION SHEET
SUMMARY
REVIEW QUESTIONS
ANSWER KEY
9 Evaluating AI/ML Platforms and Services
BENEFITS AND FACTORS TO CONSIDER WHEN CHOOSING AN AI/ML SERVICE
AWS AI AND ML SERVICES
CORE AI SERVICES
SPECIALIZED AI SERVICES
MACHINE LEARNING SERVICES
THE GOOGLE AI/ML SERVICES STACK
THE MICROSOFT AI/ ML SERVICES STACK
OTHER ENTERPRISE CLOUD AI PLATFORMS
WORKBOOK TEMPLATE: AI/ML PLATFORM EVALUATION SHEET
SUMMARY
REVIEW QUESTIONS
ANSWER KEY
10 Launching Your Pilot Project
LAUNCHING YOUR PILOT
FOLLOWING THE MACHINE LEARNING
LIFECYCLE
WORKBOOK TEMPLATE: AI/ML PILOT LAUNCH CHECKLIST
SUMMARY
REVIEW QUESTIONS
ANSWER KEY
PART IV: Building and Governing Your Team
11 Empowering Your People Through Org Change Management
SUCCEEDING THROUGH A PEOPLE-CENTRIC APPROACH
ALIGNING YOUR ORGANIZATION AROUND AI ADOPTION TO ACHIEVE BUSINESS OUTCOMES
WORKBOOK TEMPLATE: ORG CHANGE MANAGEMENT PLAN
SUMMARY
REVIEW QUESTIONS
ANSWER KEY
NOTE
12 Building Your Team
UNDERSTANDING THE ROLES AND RESPONSIBILITIES IN AN ML PROJECT
WORKBOOK TEMPLATE: TEAM BUILDING MATRIX
SUMMARY
REVIEW QUESTIONS
ANSWER KEY
PART V: Setting Up Infrastructure and Managing Operations
13 Setting Up an Enterprise AI Cloud Platform Infrastructure
REFERENCE ARCHITECTURE PATTERNS FOR TYPICAL USE CASES
FACTORS TO CONSIDER WHEN BUILDING AN ML PLATFORM
KEY COMPONENTS OF AN ML AND DL PLATFORM
KEY COMPONENTS OF AN ENTERPRISE AI/ML HEALTHCARE PLATFORM
WORKBOOK TEMPLATE: ENTERPRISE AI CLOUD PLATFORM SETUP CHECKLIST
SUMMARY
REVIEW QUESTIONS
ANSWER KEY
14 Operating Your AI Platform with MLOps Best Practices
CENTRAL ROLE OF MLOps IN BRIDGING INFRASTRUCTURE, DATA, AND MODELS
MODEL OPERATIONALIZATION
WORKBOOK TEMPLATE: ML OPERATIONS
AUTOMATION GUIDE
SUMMARY
REVIEW QUESTIONS
ANSWER KEY
PART VI: Processing Data and Modeling
15 Process Data and Engineer Features in the Cloud
UNDERSTANDING YOUR DATA NEEDS
BENEFITS AND CHALLENGES OF CLOUD-BASED DATA PROCESSING
THE DATA PROCESSING PHASES OF THE ML LIFECYCLE
UNDERSTANDING THE DATA EXPLORATION AND PREPROCESSING STAGE
FEATURE ENGINEERING
WORKBOOK TEMPLATE: DATA PROCESSING & FEATURE ENGINEERING WORKFLOW
SUMMARY
REVIEW QUESTIONS
ANSWER KEY
16 Choosing Your AI/ML Algorithms
BACK TO THE BASICS: WHAT IS ARTIFICIAL INTELLIGENCE?
FACTORS TO CONSIDER WHEN CHOOSING A MACHINE LEARNING ALGORITHM
DATA-DRIVEN PREDICTIONS USING MACHINE LEARNING
THE AI/ML FRAMEWORK
WORKBOOK TEMPLATE: AI/ML ALGORITHM SELECTION GUIDE
SUMMARY
REVIEW QUESTIONS
ANSWER KEY
17 Training, Tuning, and Evaluating Models
MODEL BUILDING
MODEL TRAINING
MODEL TUNING
MODEL VALIDATION
MODEL EVALUATION
BEST PRACTICES
WORKBOOK TEMPLATE: MODEL TRAINING AND EVALUATION SHEET
SUMMARY
REVIEW QUESTIONS
ANSWER KEY
PART VII: Deploying and Monitoring Models
18 Deploying Your Models Into Production
STANDARDIZING MODEL DEPLOYMENT, MONITORING, AND GOVERNANCE
DEPLOYING YOUR MODELS
SYNCHRONIZING ARCHITECTURE AND CONFIGURATION ACROSS ENVIRONMENTS
MLOps AUTOMATION: IMPLEMENTING CI/CD FOR MODELS
WORKBOOK TEMPLATE: MODEL DEPLOYMENT PLAN
SUMMARY
REVIEW QUESTIONS
ANSWER KEY
19 Monitoring Models
MONITORING MODELS
KEY STRATEGIES FOR MONITORING ML MODELS
TRACKING KEY MODEL PERFORMANCE METRICS
REAL-TIME VS. BATCH MONITORING
TOOLS FOR MONITORING MODELS
BUILDING A MODEL MONITORING SYSTEM
MONITORING MODEL ENDPOINTS
OPTIMIZING MODEL PERFORMANCE
WORKBOOK TEMPLATE: MODEL MONITORING TRACKING SHEET
SUMMARY
REVIEW QUESTIONS
ANSWER KEY
20 Governing Models for Bias and Ethics
IMPORTANCE OF MODEL GOVERNANCE STRATEGIES FOR FAIRNESS
OPERATIONALIZING GOVERNANCE
WORKBOOK TEMPLATE: MODEL GOVERNANCE FOR BIAS & ETHICS CHECKLIST
SUMMARY
REVIEW QUESTIONS
ANSWER KEY
PART VIII: Scaling and Transforming AI
21 Using the AI Maturity Framework to Transform Your Business
SCALING AI TO BECOME AN AI-FIRST COMPANY
THE AI MATURITY FRAMEWORK
WORKBOOK TEMPLATE: AI MATURITY
ASSESSMENT TOOL
SUMMARY
REVIEW QUESTIONS
ANSWER KEY
22 Setting Up Your AI COE
SCALING AI TO BECOME AN AI-FIRST COMPANY
ESTABLISHING AN AI CENTER OF EXCELLENCE
WORKBOOK TEMPLATE: AI CENTER OF EXCELLENCE (AICOE) SETUP CHECKLIST
SUMMARY
REVIEW QUESTIONS
ANSWER KEY
23 Building Your AI Operating Model and Transformation Plan
UNDERSTANDING THE AI OPERATING MODEL
IMPLEMENTING YOUR AI OPERATING MODEL
WORKBOOK TEMPLATE: AI OPERATING MODEL AND TRANSFORMATION PLAN
SUMMARY
REVIEW QUESTIONS
ANSWER KEY
PART IX: Evolving and Maturing AI
24 Implementing Generative AI Use Cases with ChatGPT for the Enterprise
THE RISE AND REACH OF GENERATIVE AI
THE POWER OF GENERATIVE AI/ChatGPT FOR BUSINESS TRANSFORMATION AND INNOVATION
IMPLEMENTING GENERATIVE AI AND ChatGPT
BEST PRACTICES WHEN IMPLEMENTING GENERATIVE AI AND ChatGPT
GENERATIVE AI CLOUD PLATFORMS
WORKBOOK TEMPLATE: GENERATIVE AI USE CASE PLANNER
SUMMARY
REVIEW QUESTIONS
ANSWER KEY
25 Planning for the Future of AI
EMERGING AI TRENDS
THE PRODUCTIVITY REVOLUTION
CRITICAL ENABLERS
EMERGING TRENDS IN DATA MANAGEMENT
WORKBOOK TEMPLATE: FUTURE OF AI ROADMAP
SUMMARY
REVIEW QUESTIONS
ANSWER KEY
26 Continuing Your AI Journey
REFLECTING ON YOUR PROGRESS
PLANNING FOR THE FUTURE: BUILDING A ROADMAP
ENSURING RESPONSIBLE AI/ML IMPLEMENTATION
PREPARING FOR THE CHALLENGES AHEAD
INDEX
COPYRIGHT
DEDICATION
ACKNOWLEDGMENTS
ABOUT THE AUTHOR
ABOUT THE TECHNICAL EDITOR
END USER LICENSE AGREEMENT
List of Tables
Chapter 1
TABLE 1.1: Companies Leading the Way in Adopting AI
Chapter 3
TABLE 3.1: AI Transformation Challenges
Chapter 6
TABLE 6.1: AI Strategy and Portfolio Deliverables
Chapter 9
TABLE 9.1: Differences Between Machine Learning Algorithms, Models, and Serv...
TABLE 9.2: Comparison of Amazon Forecast Models for Various Use Cases
TABLE 9.3: Google AI/ML Services for Developers
Chapter 16
TABLE 16.1: Supervised and Unsupervised Machine Learning Algorithms
TABLE 16.2: Use Cases for Linear Regression Algorithms
TABLE 16.3: Use Cases for Logistic Regression
TABLE 16.4: Use Cases for Decision Tree Algorithms
TABLE 16.5: Use Cases for Support Vector Machines
TABLE 16.6: Use Cases for Autoencoders
TABLE 16.7: TensorFlow vs. PyTorch ML Frameworks
Chapter 17
TABLE 17.1: Sample Confusion Matrix
TABLE 17.2: Sample Results Predicted by the Model
TABLE 17.3: Confusion Matrix with Values
Chapter 21
TABLE 21.1: The Stages of AI Maturity for Strategy and Planning
TABLE 21.2: The Stages of AI Maturity for People
TABLE 21.3: The Stages of AI Maturity for Platform & Operations
TABLE 21.4: The Stages of AI Maturity for Data/Models
Chapter 24
TABLE 24.1: Artifacts That Can Be Created by Generative AI
TABLE 24.2: Discriminative vs. Generative Models
TABLE 24.3: The Differences Between Generative Language and Generative Image...
TABLE 24.4: Difference Between Foundation and Language Models
TABLE 24.5: Differences Between GANs, VAEs, and Diffusion Models
TABLE 24.6: Base Models Grouped by Family and Capability in Azure
List of Illustrations
Chapter 1
FIGURE 1.1: Comparison of enterprise-wide AI adoption by leaders and others...
FIGURE 1.2: An AI-first strategy leads to an enterprise transformation.
FIGURE 1.3: The triumvirate of AI: cloud computing, big data, and software a...
FIGURE 1.4: The network effect
FIGURE 1.5: Enterprise AI opportunities
Chapter 2
FIGURE 2.1: Challenges, benefits, and solutions adopted by the U.S. governme...
FIGURE 2.2: Challenges, benefits, and solutions adopted by Capital One
FIGURE 2.3: Capital One's transformation journey to AIfirst status
FIGURE 2.4: The path to becoming world-class
Chapter 3
FIGURE 3.1: STRATEGIZE AND PREPARE: Address the challenges with AI
FIGURE 3.2: Skill sets required in a typical AI/ML project
FIGURE 3.3: A typical data infrastructure pipeline
Chapter 4
FIGURE 4.1: STRATEGIZE AND PREPARE: Design AI systems responsibly
FIGURE 4.2: Key pillars of Responsible AI
FIGURE 4.3: Collaborative AI: Enhancing human capacity with AI
FIGURE 4.4: Key elements of trustworthy AI
FIGURE 4.5: Scalable AI: Key considerations and case study
FIGURE 4.6: Building human-centric AI systems with human values at the foref...
Chapter 5
FIGURE 5.1: STRATEGIZE AND PREPARE: Envision and align
FIGURE 5.2: Steps to implement enterprise AI
FIGURE 5.3: Envision phase: tasks and deliverables
FIGURE 5.4: Align phase: tasks and deliverables
Chapter 6
FIGURE 6.1: STRATEGIZE AND PREPARE: Develop business case and AI strategy
FIGURE 6.2: Capability focus areas for enterprise AI
FIGURE 6.3: Business focus areas for strategy and planning
FIGURE 6.4: Examples of business strategy, AI strategy, business goals, and ...
FIGURE 6.5: AI strategy: phases and capabilities
FIGURE 6.6: AI execution: phases and capabilities
Chapter 7
FIGURE 7.1: STRATEGIZE AND PREPARE: Manage strategic change
FIGURE 7.2: AI change acceleration strategy: phases and capabilities
FIGURE 7.3: Develop AI acceleration charter and governance mechanisms
FIGURE 7.4: Transform your leadership: Envision and Align phases
FIGURE 7.5: Transform your leadership: Launch and Scale phases
FIGURE 7.6: Transform your workspace: tasks and deliverables
FIGURE 7.7: Ensuring leadership alignment for AI, including generative AI in...
FIGURE 7.8: AI strategy: phases and capabilities
Chapter 8
FIGURE 8.1: PLAN AND LAUNCH: Identify Use Cases for Your AI/ML & Gen AI Proj...
FIGURE 8.2: Use case identification process flow
FIGURE 8.3: Defining business objectives for your AI initiative
FIGURE 8.4: Define success metrics for your AI initiative
FIGURE 8.5: Business value and feasibility analysis to prioritize use cases...
FIGURE 8.6: Digital twin of jet airplane engine
FIGURE 8.7: The benefits of intelligent search
FIGURE 8.8: Machine learning modernization framework, benefits, and technolo...
FIGURE 8.9: Applications and technologies behind computer vision
FIGURE 8.10: Data types currently supported by generative AI
Chapter 9
FIGURE 9.1: PLAN AND LAUNCH: Evaluate AI/ML Platforms & Services
FIGURE 9.2: AWS AI/ML stack
FIGURE 9.3: AWS core AI services
FIGURE 9.4: Amazon Textract use cases
FIGURE 9.5: Amazon Lex: Conversational interfaces using voice and text
FIGURE 9.6: AWS specialized AI services
FIGURE 9.7: How Amazon Forecast works
FIGURE 9.8: How Amazon Kendra works
FIGURE 9.9: Amazon CodeGuru uses machine learning to improve app code.
FIGURE 9.10: Amazon industrial AI solutions
FIGURE 9.11: AWS ML services
FIGURE 9.12: SageMaker's capabilities
FIGURE 9.13: Google AI/ML stack for data scientists
FIGURE 9.14: Azure-applied AI services
FIGURE 9.15: Using Azure Video Indexer tool
Chapter 10
FIGURE 10.1: PLAN AND LAUNCH: Launch your AI pilot
FIGURE 10.2: Activities to move from Envision to Align to launching a pilot...
FIGURE 10.3: Machine learning phases
Chapter 11
FIGURE 11.1: BUILD AND GOVERN YOUR TEAM: Empower Your People Through Org Cha...
FIGURE 11.2: Change management focus areas for enterprise AI
FIGURE 11.3: Percentage of employees experiencing cultural tension due to ch...
FIGURE 11.4: Evolve your culture: phases and tasks
FIGURE 11.5: Evolve your culture: phases and deliverables
FIGURE 11.6: Redesign your organization: tasks and deliverables
FIGURE 11.7: Organizational alignment
Chapter 12
FIGURE 12.1: BUILD AND GOVERN YOUR TEAM: Building your team
FIGURE 12.2: Core and auxiliary roles in a machine learning project
Chapter 13
FIGURE 13.1: Setting up the enterprise AI cloud platform infrastructure
FIGURE 13.2: Customer 360-degree architecture
FIGURE 13.3: Customer 360-degree architecture using AWS components (AWS comp...
FIGURE 13.4: Event-driven near real-time predictive analytics using IoT data...
FIGURE 13.5: IoT-based event-driven predictive analytics using AWS component...
FIGURE 13.6: Personalized recommendation architecture
FIGURE 13.7: Personalized recommendation architecture using AWS components (...
FIGURE 13.8: Real-time customer engagement architecture
FIGURE 13.9: Real-time customer engagement architecture on AWS (AWS componen...
FIGURE 13.10: Real-time customer engagement architecture on Azure
FIGURE 13.11: Fraud detection architecture
FIGURE 13.12: Fraud detection architecture on AWS (AWS components shown in b...
FIGURE 13.13: Basic components and their integrations in an AI/ML platform
FIGURE 13.14: Data management architecture
FIGURE 13.15: Components of an ML experimentation platform
FIGURE 13.16: Hybrid and edge computing in machine learning
Chapter
14
FIGURE 14.1: SETUP INFRASTRUCTURE AND MANAGE OPERATIONS: Automate AI platfor...
FIGURE 14.2: CI/CD flow for model training and deployment
FIGURE 14.3: CI/CD pipeline for ML training and deployment on AWS
FIGURE 14.4: Code deployment pipeline
FIGURE 14.5: Centralized model inventory management
FIGURE 14.6: Logging and auditing architecture
FIGURE 14.7: Data and artifacts lineage tracking
FIGURE 14.8: Using tags to track resource usage, cost management, billing, a...
Chapter 15
FIGURE 15.1: PROCESS DATA AND MODELING: Process data and engineer features i...
FIGURE 15.2: Data types
FIGURE 15.3: Data collection process
FIGURE 15.4: Average time spent in ML tasks
FIGURE 15.5: Data processing workflow
FIGURE 15.6: Data preprocessing strategies
FIGURE 15.7: Feature engineering components
FIGURE 15.8: Feature extraction techniques
FIGURE 15.9: Feature imputation techniques
Chapter 16
FIGURE 16.1: PROCESS DATA AND MODELING: Choose your AI/ML algorithm
FIGURE 16.2: Umbrella of AI technologies
FIGURE 16.3: Use cases of supervised and unsupervised learning tasks
FIGURE 16.4: How supervised learning works
FIGURE 16.5: How supervised learning worksSource: (a) Kate / Adobe Systems I...
FIGURE 16.6: Types of supervised learning
FIGURE 16.7: Example of linear regression with one independent variable
FIGURE 16.8: Example logistic regression graph showing the probability of cu...
FIGURE 16.9: Decision tree that shows the survival of passengers on the Tita...
FIGURE 16.10: A simplified view of a random forest
FIGURE 16.11: Support vector machine trained from two samples
FIGURE 16.12: Example of K-NN classification
FIGURE 16.13: How unsupervised learning works
FIGURE 16.14: K-means clusters created from an Iris flower dataset
FIGURE 16.15: Interpreting the PCA: The start of the bend indicates that thr...
FIGURE 16.16: Schema of a basic autoencoder
FIGURE 16.17: Collaborative filtering based on a rating system
FIGURE 16.18: Reinforcement learning is when an agent takes action in an env...
FIGURE 16.19: Deep learning is different from machine learning and tradition...
FIGURE 16.20: Typical CNN architecture
FIGURE 16.21: GAN model
Chapter 17
FIGURE 17.1: Training, tuning, and evaluating models
FIGURE 17.2: Phases of a model development lifecycle
FIGURE 17.3: Model training and tuning components
FIGURE 17.4: Problems faced when training models
FIGURE 17.5: Model artifacts outputted during model training
FIGURE 17.6: Tuning hyperparameters for optimal model performance
FIGURE 17.7: Grid versus random searches
FIGURE 17.8: Validating machine learning models
FIGURE 17.9: Validation across ML model development phases
FIGURE 17.10: Validation metrics for classification problems
FIGURE 17.11: Performance evaluation pipeline
FIGURE 17.12: Machine learning security best practices
FIGURE 17.13: Reliability best practices
FIGURE 17.14: Cost optimization best practices
Chapter 18
FIGURE 18.1: DEPLOY AND MONITOR MODELS: Deploy your models into production....
FIGURE 18.2: Model deployment process
FIGURE 18.3: Model deployment options
FIGURE 18.4: Choosing a deployment strategy
FIGURE 18.5: Different steps in an inference pipeline
FIGURE 18.6: Implementing CI/CD for models
Chapter 19
FIGURE 19.1: Monitoring models
FIGURE 19.2: Key strategies of monitoring
FIGURE 19.3: Choosing model performance metrics
FIGURE 19.4: Monitoring the health of your endpoints
FIGURE 19.5: Implementing a recoverable endpoint
FIGURE 19.6: ML lifecycle
Chapter 20
FIGURE 20.1: DEPLOY AND GOVERN MODELS: Govern models for bias and ethics
FIGURE 20.2: Strategies to ensure fairness in models
FIGURE 20.3: Ethical considerations when deploying models
FIGURE 20.4: Different benefits of artifacts management
FIGURE 20.5: Central repository of various machine learning artifacts
FIGURE 20.6: Setting up a model governance framework
Chapter 21
FIGURE 21.1: SCALE AND TRANSFORM: Use the AI Maturity Framework to transform...
FIGURE 21.2: Strategic pillars for an AI-first strategy
FIGURE 21.3: Sample AI Maturity Framework result
FIGURE 21.4: Five stages of the AI Maturity Framework
FIGURE 21.5: Key elements of the Optimizing stage
FIGURE 21.6: Maturity levels for the people dimension
Chapter 22
FIGURE 22.1: SCALE AND TRANSFORM: Set up your AI COE
FIGURE 22.2: AI core team responsibilities
FIGURE 22.3: Evolution of an AI COE
Chapter 23
FIGURE 23.1: SCALE AND TRANSFORM: Build your AI operating model and transfor...
FIGURE 23.2: Components of an AI operating model
FIGURE 23.3: Six transformational ways to build an AI operating model
FIGURE 23.4: Customer-centric AI strategy to drive innovation
FIGURE 23.5: A product-centric approach to building AI solutions
FIGURE 23.6: Organizing teams around the product
FIGURE 23.7: Start small and build iteratively with crossfunctional teams
FIGURE 23.8: AI product testing and measurement framework
FIGURE 23.9: Aligning operating model with strategic value and establishing ...
FIGURE 23.10: Different components of an AI transformation plan
Chapter 24
FIGURE 24.1: EVOLVE AND MATURE: Generative AI and ChatGPT use cases for your...
FIGURE 24.2: Generative AI is a subset of deep learning
FIGURE 24.3: Structure of artificial neural networks
FIGURE 24.4: Inputs and outputs of a foundation model
FIGURE 24.5: The AI-powered content generation use case
FIGURE 24.6: Generative AI applications
FIGURE 24.7: Long-term workforce transformation with AI as a partner
FIGURE 24.8: Components of a transformer model
FIGURE 24.9: Build versus buy options when implementing generative AI
FIGURE 24.10: Using prompts to train LLMs
FIGURE 24.11: Retrieval augmented generation architecture
FIGURE 24.12: Strategy best practices for implementing Generative AI
FIGURE 24.13: Challenges of implementing generative AI
FIGURE 24.14: Mitigating generative AI risks
FIGURE 24.15: Foundation models in the model garden
FIGURE 24.16: AWS Generative AI cloud platform tools
FIGURE 24.17: AWS CodeWhisperer workflow
FIGURE 24.18: Features of AWS Inferentia and Trainium
FIGURE 24.19: Provisioning an OpenAI service in Azure
FIGURE 24.20: Azure OpenAI Studio playgrounds for model testing
FIGURE 24.21: GitHub Copilot's features and benefits
FIGURE 24.22: Additional gen AI tools and platforms
Chapter 25
FIGURE 25.1: EVOLVE AND MATURE: Plan for the future of AI
FIGURE 25.2: Intelligent apps provide intelligent services to users.
FIGURE 25.3: Critical enablers for enterprise AI
FIGURE 25.4: Knowledge graph reflecting complex realworld data as meaningf...
Chapter 26
FIGURE 26.1: Continue your AI journey
Introduction
WELCOME TO Enterprise AI in the Cloud: A Practical Guide to Deploying End-to-End Machine Learning and ChatGPT Solutions. This book is the definitive guide to equip readers with the methodology and tools necessary to implement artificial intelligence (AI), machine learning (ML), and generative AI technologies. You have in your hands a powerful guide to potentially transform your company and your own career.
In this book, you learn how to
Develop AI strategy, solve challenges, and drive change
Identify and prioritize AI use cases, evaluate AI/ML platforms, and launch a pilot project
Build a dream team, empower people, and manage projects effectively
Set up an AI infrastructure using the major cloud platforms and scale your operations using MLOps
Process and engineer data and deploy, operate, and monitor AI models in production
Use govern models and implement AI ethically and responsibly
Scale your AI effort by setting up an AI center of excellence (AI COE), an AI operating model, and an enterprise transformation plan
Evolve your company using generative AI such as ChatGPT, plan for the future, and continuously innovate with AI
From real-world AI implementation, AI/ML use cases, and hands-on labs to nontechnical aspects such as team development and AI-first strategy, this book has it all.
In a nutshell, this book is a comprehensive guide that bridges the gap between theory and real-world AI deployments. It's a blend of strategy and tactics, challenges, and solutions that make it an
indispensable resource for those interested in building and operating AI systems for their enterprise.
HOW THIS BOOK IS ORGANIZED
This book is not just a theoretical guide but a practical, hands-on manual to transform your business through AI in the cloud. My aim is to provide you with the tools and knowledge you need to harness the power of AI for your enterprise with a methodology that is comprehensive and deep.
Part I: Introduction: In Part I, I explain how enterprises are undergoing transformation through the adoption of AI using cloud technologies. I cover industry use cases for AI in the cloud and its benefits, as well as the current state of AI transformation. I also discuss various case studies of successful AI implementations, including the U.S. government, Capital One, and Netflix.
Part II: Strategizing and Assessing for AI: In this part, I discuss the nitty-gritty of AI, such as the challenges you may face during the AI journey, along with the ethical concerns, and the four phases that you can adopt to build your AI capabilities. I then discuss using a roadmap to develop an AI strategy, finding the best use cases for your project, and evaluating the AI/ML platforms and services from various cloud providers. It's like a step-by-step guide to your AI adventure.
Part III: Planning and Launching a Pilot Project: This part covers all the challenges and tasks centered on planning and launching a pilot project, including identifying use cases for your project, evaluating appropriate platforms and services, and launching the actual project.
Part IV: Building and Governing Your Team: People make magic happen! Part IV explores the organizational changes required to empower your workforce. I guide you through the steps to launching your pilot and assembling your dream team. It's all about nurturing the human side of things.
Part V: Setting Up Infrastructure and Managing Operations: In this part, you roll up your sleeves and get technical. Part V is like your DIY guide to building your own AI/ML platform. Here, I discuss the technical requirements and
the daily operations of the platform with a focus on automation and scale. This part is a hands-on toolkit for those who are hungry to get geeky.
Part VI: Processing Data and Modeling: Data is the lifeblood of AI. Part VI is where you get your hands dirty with data and modeling. I teach you how to process data in the cloud, choose the right AI/ML algorithm based on your use case, and get your models trained, tuned, and evaluated. It is where the science meets the art.
Part VII: Deploying and Monitoring Models: Yay! It is launching time. Part VII guides you through the process of deploying the model into production for consumption. I also discuss the nuances of monitoring, securing, and governing models so they are working smoothly, safely, and securely.
Part VIII: Scaling and Transforming AI: You have built it, so now you can make it even bigger! In Part VIII, I present a roadmap to scale your AI transformation. I discuss how to take your game to the next level by introducing the AI maturity framework and establishing an AI COE. I also guide you through the process of building an AI operating model and transformation plan. This is where AI transitions from the project level to an enterprise-level powerhouse.
Part IX: Evolving and Maturing AI: This is where you peek into a crystal ball. I delve into the exciting world of generative AI, discuss where the AI space is headed, and provide guidance on how to continue your AI journey.
WHO SHOULD READ THIS BOOK?
This book is primarily meant for those serious about implementing AI at the enterprise level. It is for those focused on understanding and implementing AI within the context of enabling and executing upon an enterprise-wide AI strategy. It is a substantive, content-rich resource that requires focused reading and note-taking, with takeaways that you can go back to your company and start implementing. Below are some examples of roles that will benefit.