Reliable Machine Learning

Spis treści książki

• Foreword
• Preface
  • Why We Wrote This Book
  • SRE as the Lens on ML
  • Intended Audience
  • How This Book Is Organized
    • Our Approach
    • Lets Knit!
    • Navigating This Book
  • About the Authors
  • Conventions Used in This Book
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
    • Cathy Chen
    • Niall Richard Murphy
    • Kranti Parisa
    • D. Sculley
    • Todd Underwood
• 1. Introduction
  • The ML Lifecycle
    • Data Collection and Analysis
    • ML Training Pipelines
    • Build and Validate Applications
    • Quality and Performance Evaluation
    • Defining and Measuring SLOs
    • Launch
      • Models as code
      • Launch slowly
      • Release, not refactor
      • Isolate rollouts at the data layer
      • Measure SLOs during launch
      • Review the rollout
    • Monitoring and Feedback Loops
  • Lessons from the Loop
• 2. Data Management Principles
  • Data as Liability
  • The Data Sensitivity of ML Pipelines
  • Phases of Data
    • Creation
    • Ingestion
    • Processing
      • Validation
      • Cleaning and ensuring data consistency
      • Enriching and extending
    • Storage
    • Management
    • Analysis and Visualization
  • Data Reliability
    • Durability
    • Consistency
    • Version Control
    • Performance
    • Availability
  • Data Integrity
    • Security
    • Privacy
    • Policy and Compliance
      • Jurisdictional rules
      • Reporting requirements
  • Conclusion
• 3. Basic Introduction to Models
  • What Is a Model?
  • A Basic Model Creation Workflow
  • Model Architecture Versus Model Definition Versus Trained Model
  • Where Are the Vulnerabilities?
    • Training Data
      • Incomplete coverage
      • Spurious correlations
      • Cold start
      • Self-fulfilling prophecies and ML echo chambers
      • Changes in the world
    • Labels
      • Label noise
      • Wrong label objective
      • Fraud or malicious feedback
    • Training Methods
      • Overfitting
      • Lack of stability
      • Peculiarities of deep learning
  • Infrastructure and Pipelines
    • Platforms
    • Feature Generation
    • Upgrades and Fixes
  • A Set of Useful Questions to Ask About Any Model
  • An Example ML System
    • Yarn Product Click-Prediction Model
    • Features
    • Labels for Features
    • Model Updating
    • Model Serving
    • Common Failures
  • Conclusion
• 4. Feature and Training Data
  • Features
    • Feature Selection and Engineering
    • Lifecycle of a Feature
    • Feature Systems
      • Data ingestion system
      • Feature store
      • Feature quality evaluation system
  • Labels
  • Human-Generated Labels
    • Annotation Workforces
    • Measuring Human Annotation Quality
    • An Annotation Platform
    • Active Learning and AI-Assisted Labeling
    • Documentation and Training for Labelers
  • Metadata
    • Metadata Systems Overview
    • Dataset Metadata
    • Feature Metadata
    • Label Metadata
    • Pipeline Metadata
  • Data Privacy and Fairness
    • Privacy
      • PII data and features
      • Private data and labeling
    • Fairness
  • Conclusion
• 5. Evaluating Model Validity and Quality
  • Evaluating Model Validity
  • Evaluating Model Quality
    • Offline Evaluations
    • Evaluation Distributions
      • Held-out test data
      • Progressive validation
      • Golden sets
      • Stress-test distributions
      • Sliced analysis
      • Counterfactual testing
    • A Few Useful Metrics
      • Canary metrics
        • Bias
        • Calibration
      • Classification metrics
        • Accuracy
        • Precision and recall
        • AUC ROC
        • Precision/recall curves
      • Regression metrics
        • Mean squared error and mean absolute error
        • Log loss
  • Operationalizing Verification and Evaluation
  • Conclusion
• 6. Fairness, Privacy, and Ethical ML Systems
  • Fairness (a.k.a. Fighting Bias)
    • Definitions of Fairness
    • Reaching Fairness
    • Fairness as a Process Rather than an Endpoint
    • A Quick Legal Note
  • Privacy
    • Methods to Preserve Privacy
      • Technical measures
      • Institutional measures
    • A Quick Legal Note
  • Responsible AI
    • Explanation
    • Effectiveness
    • Social and Cultural Appropriateness
  • Responsible AI Along the ML Pipeline
    • Use Case Brainstorming
    • Data Collection and Cleaning
    • Model Creation and Training
    • Model Validation and Quality Assessment
    • Model Deployment
    • Products for the Market
  • Conclusion
• 7. Training Systems
  • Requirements
  • Basic Training System Implementation
    • Features
    • Feature Store
    • Model Management System
    • Orchestration
      • Job/process/resource scheduling system
      • ML framework
    • Quality Evaluation
    • Monitoring
  • General Reliability Principles
    • Most Failures Will Not Be ML Failures
    • Models Will Be Retrained
    • Models Will Have Multiple Versions (at the Same Time!)
    • Good Models Will Become Bad
    • Data Will Be Unavailable
    • Models Should Be Improvable
    • Features Will Be Added and Changed
    • Models Can Train Too Fast
    • Resource Utilization Matters
    • Utilization != Efficiency
    • Outages Include Recovery
  • Common Training Reliability Problems
    • Data Sensitivity
    • Example Data Problem at YarnIt
    • Reproducibility
    • Example Reproducibility Problem at YarnIt
    • Compute Resource Capacity
    • Example Capacity Problem at YarnIt
  • Structural Reliability
    • Organizational Challenges
    • Ethics and Fairness Considerations
  • Conclusion
• 8. Serving
  • Key Questions for Model Serving
    • What Will Be the Load to Our Model?
    • What Are the Prediction Latency Needs of Our Model?
    • Where Does the Model Need to Live?
      • On a local machine
      • On servers owned or managed by our organization
      • In the cloud
      • On-device
    • What Are the Hardware Needs for Our Model?
    • How Will the Serving Model Be Stored, Loaded, Versioned, and Updated?
    • What Will Our Feature Pipeline for Serving Look Like?
  • Model Serving Architectures
    • Offline Serving (Batch Inference)
      • Advantages
      • Disadvantages
    • Online Serving (Online Inference)
      • Advantages
      • Disadvantages
    • Model as a Service
      • Advantages
      • Disadvantages
    • Serving at the Edge
      • Advantages
      • Disadvantages
    • Choosing an Architecture
  • Model API Design
  • Testing
  • Serving for Accuracy or Resilience?
  • Scaling
    • Autoscaling
    • Caching
  • Disaster Recovery
  • Ethics and Fairness Considerations
  • Conclusion
• 9. Monitoring and Observability for Models
  • What Is Production Monitoring and Why Do It?
    • What Does It Look Like?
    • The Concerns That ML Brings to Monitoring
    • Reasons for Continual ML Observabilityin Production
  • Problems with ML Production Monitoring
    • Difficulties of Development Versus Serving
    • A Mindset Change Is Required
  • Best Practices for ML Model Monitoring
    • Generic Pre-serving Model Recommendations
      • Explainability and monitoring
    • Training and Retraining
      • Concrete recommendations
    • Model Validation (Before Rollout)
      • Fallbacks in validation
      • Call to action
      • Concrete recommendations
    • Serving
      • Model
        • Case 1: Real-time actuals
        • Case 2: Delayed actuals
        • Case 3: Biased actuals
        • Case 4: No/few actuals
        • Other approaches
        • Troubleshooting model performance metrics
      • Data
        • Drift
        • Measuring drift
        • Troubleshooting drift
      • Data quality
        • Categorical data
        • Numerical data
        • Measuring data quality
      • Service
        • Optimizing performance of the model
        • Optimizing performance of the service
      • Other Things to Consider
        • SLOs in ML monitoring
        • Monitoring across services
        • Fairness in monitoring
        • Privacy in monitoring
        • Business impact
        • Dense data types (image, video, text documents, audio, and so on)
      • High-Level Recommendations for Monitoring Strategy
  • Conclusion
• 10. Continuous ML
  • Anatomy of a Continuous ML System
    • Training Examples
    • Training Labels
    • Filtering Out Bad Data
    • Feature Stores and Data Management
    • Updating the Model
    • Pushing Updated Models to Serving
  • Observations About Continuous ML Systems
    • External World Events May Influence Our Systems
    • Models Can Influence Their Own Training Data
    • Temporal Effects Can Arise at Several Timescales
    • Emergency Response Must Be Done in Real Time
      • Stop training
      • Fall back
      • Roll back
      • Remove bad data
      • Roll through
      • Choosing a response strategy
      • Organizational considerations
    • New Launches Require Staged Ramp-ups and Stable Baselines
    • Models Must Be Managed Rather Than Shipped
  • Continuous Organizations
  • Rethinking Noncontinuous ML Systems
  • Conclusion
• 11. Incident Response
  • Incident Management Basics
    • Life of an Incident
    • Incident Response Roles
  • Anatomy of an ML-Centric Outage
  • Terminology Reminder: Model
  • Story Time
    • Story 1: Searching but Not Finding
      • Stages of ML incident response for story 1
    • Story 2: Suddenly Useless Partners
      • Stages of ML incident response for story 2
    • Story 3: Recommend You Find New Suppliers
      • Stages of ML incident response for story 3
  • ML Incident Management Principles
    • Guiding Principles
    • Model Developer or Data Scientist
      • Preparation
      • Incident handling
      • Continuous improvement
    • Software Engineer
      • Preparation
      • Incident handling
      • Continuous improvement
    • ML SRE or Production Engineer
      • Preparation
      • Incident handling
      • Continuous improvement
    • Product Manager or Business Leader
      • Preparation
      • Incident handling
      • Continuous improvement
  • Special Topics
    • Production Engineers and ML Engineering Versus Modeling
    • The Ethical On-Call Engineer Manifesto
      • Impact
      • Cause
      • Troubleshooting
      • Solutions and a call to action
  • Conclusion
• 12. How Product and ML Interact
  • Different Types of Products
  • Agile ML?
  • ML Product Development Phases
    • Discovery and Definition
    • Business Goal Setting
    • MVP Construction and Validation
    • Model and Product Development
    • Deployment
    • Support and Maintenance
  • Build Versus Buy
    • Models
      • Generic use cases
      • Companys data initiatives
    • Data Processing Infrastructure
    • End-to-End Platforms
    • Scoring Approach for Making the Decision
    • Making the Decision
  • Sample YarnIt Store Features Powered by ML
    • Showcasing Popular Yarns by Total Sales
    • Recommendations Based on Browsing History
    • Cross-selling and Upselling
    • Content-Based Filtering
    • Collaborative Filtering
  • Conclusion
• 13. Integrating ML into Your Organization
  • Chapter Assumptions
    • Leader-Based Viewpoint
    • Detail Matters
    • ML Needs to Know About the Business
    • The Most Important Assumption You Make
    • The Value of ML
  • Significant Organizational Risks
    • ML Is Not Magic
    • Mental (Way of Thinking) Model Inertia
    • Surfacing Risk Correctly in Different Cultures
    • Siloed Teams Dont Solve All Problems
  • Implementation Models
    • Remembering the Goal
    • Greenfield Versus Brownfield
    • ML Roles and Responsibilities
    • How to Hire ML Folks
  • Organizational Design and Incentives
    • Strategy
    • Structure
    • Processes
    • Rewards
    • People
    • A Note on Sequencing
  • Conclusion
• 14. Practical ML Org Implementation Examples
  • Scenario 1: A New Centralized ML Team
    • Background and Organizational Description
    • Process
    • Rewards
    • People
    • Default Implementation
  • Scenario 2: Decentralized ML Infrastructure and Expertise
    • Background and Organizational Description
    • Process
    • Rewards
    • People
    • Default Implementation
  • Scenario 3: Hybrid with Centralized Infrastructure/Decentralized Modeling
    • Background and Organizational Description
    • Process
    • Rewards
    • People
    • Default Implementation
  • Conclusion
• 15. Case Studies: MLOps in Practice
  • 1. Accommodating Privacy and Data Retention Policies in ML Pipelines
    • Background
    • Problem and Resolution
      • Challenge 1: Which dialects?
      • Solution: Get rid of the concept of dialects!
      • Challenge 2: Racing the clock
      • Solutions (and new challenges!)
    • Takeaways
  • 2. Continuous ML Model Impacting Traffic
    • Background
    • Problem and Resolution
    • Takeaways
  • 3. Steel Inspection
    • Background
    • Problem and Resolution
    • Takeaways
  • 4. NLP MLOps: Profiling and Staging Load Test
    • Background
    • Problem and Resolution
      • An improved process for benchmarking
    • Takeaways
  • 5. Ad Click Prediction: Databases Versus Reality
    • Background
    • Problem and Resolution
    • Takeaways
  • 6. Testing and Measuring Dependencies in ML Workflow
    • Background
    • Problem and Resolution
      • Building the regression-testing sandbox
      • Monitoring for regression
    • Takeaways
• Index

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