Introducing MLOps

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• Preface
  • Who This Book Is For
  • How This Book Is Organized
  • Conventions Used in This Book
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
• I. MLOps: What and Why
• 1. Why Now and Challenges
  • Defining MLOps and Its Challenges
  • MLOps to Mitigate Risk
    • Risk Assessment
    • Risk Mitigation
    • MLOps for Responsible AI
  • MLOps for Scale
  • Closing Thoughts
• 2. People of MLOps
  • Subject Matter Experts
  • Data Scientists
  • Data Engineers
  • Software Engineers
  • DevOps
  • Model Risk Manager/Auditor
  • Machine Learning Architect
  • Closing Thoughts
• 3. Key MLOps Features
  • A Primer on Machine Learning
  • Model Development
    • Establishing Business Objectives
    • Data Sources and Exploratory Data Analysis
    • Feature Engineering and Selection
    • Training and Evaluation
    • Reproducibility
    • Responsible AI
  • Productionalization and Deployment
    • Model Deployment Types and Contents
    • Model Deployment Requirements
  • Monitoring
    • DevOps Concerns
    • Data Scientist Concerns
      • Ground truth
      • Input drift
    • Business Concerns
  • Iteration and Life Cycle
    • Iteration
    • The Feedback Loop
  • Governance
    • Data Governance
    • Process Governance
  • Closing Thoughts
• II. MLOps: How
• 4. Developing Models
  • What Is a Machine Learning Model?
    • In Theory
    • In Practice
    • Required Components
    • Different ML Algorithms, Different MLOps Challenges
  • Data Exploration
  • Feature Engineering and Selection
    • Feature Engineering Techniques
    • How Feature Selection Impacts MLOps Strategy
  • Experimentation
  • Evaluating and Comparing Models
    • Choosing Evaluation Metrics
    • Cross-Checking Model Behavior
    • Impact of Responsible AI on Modeling
  • Version Management and Reproducibility
  • Closing Thoughts
• 5. Preparing for Production
  • Runtime Environments
    • Adaptation from Development to Production Environments
      • Tooling considerations
      • Performance considerations
    • Data Access Before Validation and Launch to Production
    • Final Thoughts on Runtime Environments
  • Model Risk Evaluation
    • The Purpose of Model Validation
    • The Origins of ML Model Risk
  • Quality Assurance for Machine Learning
  • Key Testing Considerations
  • Reproducibility and Auditability
  • Machine Learning Security
    • Adversarial Attacks
    • Other Vulnerabilities
  • Model Risk Mitigation
    • Changing Environments
    • Interactions Between Models
    • Model Misbehavior
  • Closing Thoughts
• 6. Deploying to Production
  • CI/CD Pipelines
  • Building ML Artifacts
    • Whats in an ML Artifact?
    • The Testing Pipeline
  • Deployment Strategies
    • Categories of Model Deployment
    • Considerations When Sending Models to Production
    • Maintenance in Production
  • Containerization
  • Scaling Deployments
  • Requirements and Challenges
  • Closing Thoughts
• 7. Monitoring and Feedback Loop
  • How Often Should Models Be Retrained?
  • Understanding Model Degradation
    • Ground Truth Evaluation
    • Input Drift Detection
  • Drift Detection in Practice
    • Example Causes of Data Drift
    • Input Drift Detection Techniques
      • Univariate statistical tests
      • Domain classifier
      • Interpretation of results
  • The Feedback Loop
    • Logging
    • Model Evaluation
      • Logical model
      • Model evaluation store
    • Online Evaluation
      • Champion/Challenger
      • A/B testing
  • Closing Thoughts
• 8. Model Governance
  • Who Decides What Governance the Organization Needs?
  • Matching Governance with Risk Level
  • Current Regulations Driving MLOps Governance
    • Pharmaceutical Regulation in the US: GxP
    • Financial Model Risk Management Regulation
    • GDPR and CCPA Data Privacy Regulations
  • The New Wave of AI-Specific Regulations
  • The Emergence of Responsible AI
  • Key Elements of Responsible AI
    • Element 1: Data
    • Element 2: Bias
    • Element 3: Inclusiveness
    • Element 4: Model Management at Scale
    • Element 5: Governance
  • A Template for MLOps Governance
    • Step 1: Understand and Classify the Analytics Use Cases
    • Step 2: Establish an Ethical Position
    • Step 3: Establish Responsibilities
    • Step 4: Determine Governance Policies
    • Step 5: Integrate Policies into the MLOps Process
    • Step 6: Select the Tools for Centralized Governance Management
    • Step 7: Engage and Educate
    • Step 8: Monitor and Refine
  • Closing Thoughts
• III. MLOps: Real-World Examples
• 9. MLOps in Practice: Consumer Credit Risk Management
  • Background: The Business Use Case
  • Model Development
  • Model Bias Considerations
  • Prepare for Production
  • Deploy to Production
  • Closing Thoughts
• 10. MLOps in Practice: Marketing Recommendation Engines
  • The Rise of Recommendation Engines
    • The Role of Machine Learning
    • Push or Pull?
  • Data Preparation
  • Design and Manage Experiments
  • Model Training and Deployment
    • Scalability and Customizability
    • Monitoring and Retraining Strategy
    • Real-Time Scoring
    • Ability to Turn Recommendations On and Off
  • Pipeline Structure and Deployment Strategy
  • Monitoring and Feedback
    • Retraining Models
    • Updating Models
    • Runs Overnight, Sleeps During Daytime
    • Option to Manually Control Models
    • Option to Automatically Control Models
    • Monitoring Performance
  • Closing Thoughts
• 11. MLOps in Practice: Consumption Forecast
  • Power Systems
  • Data Collection
  • Problem Definition: Machine Learning, or Not Machine Learning?
  • Spatial and Temporal Resolution
  • Implementation
  • Modeling
  • Deployment
  • Monitoring
  • Closing Thoughts
• Index

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