Effective Machine Learning Teams David Tan, Ada Leung, David Colls

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Spis treści książki

• Preface
  • Who This Book Is For
  • How This Book Is Organized
    • Part I: Product and Delivery
    • Part II: Engineering
    • Part III: Teams
  • Additional Thoughts
  • Conventions Used in This Book
  • Using Code Examples
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
    • From David Tan
    • From Ada Leung
    • From David Colls
• 1. Challenges and Better Paths in Delivering ML Solutions
  • ML: Promises and Disappointments
    • Continued Optimism in ML
    • Why ML Projects Fail
      • High-level view: Barriers to success
      • Microlevel view: Everyday impediments to success
        • Lifecycle of a story in a low-effectiveness environment
        • Lifecycle of a story in a high-effectiveness environment
  • Is There a Better Way? How Systems Thinking and Lean Can Help
    • You Cant MLOps Your Problems Away
    • See the Whole: A Systems Thinking Lens for Effective ML Delivery
    • The Five Disciplines Required for Effective ML Delivery
      • What is Lean, and why should ML practitioners care?
      • The first discipline: Product
        • Discovery
        • Prototype testing
      • The second discipline: Delivery
        • Vertically sliced work
        • Vertically sliced teams, or cross-functional teams
        • Ways of Working
        • Measuring delivery metrics
      • The third discipline: Engineering
        • Automated testing
        • Refactoring
        • Code editor effectiveness
        • Continuous delivery for ML
      • The fourth discipline: ML
        • Framing ML problems
        • ML systems design
        • Responsible AI and ML governance
      • The fifth discipline: Data
        • Closing the data collection loop
        • Data security and privacy
  • Conclusion
• I. Product and Delivery
• 2. Product and Delivery Practices for ML Teams
  • ML Product Discovery
    • Discovering Product Opportunities
    • Canvases to Define Product Opportunities
      • Data Product Canvas
      • Hypothesis Canvas
    • Techniques for Rapidly Designing, Delivering, and Testing Solutions
      • Prototypes
        • A range of prototypes
        • Technical prototypes, or proofs of concept (PoCs)
      • Riskiest Assumption Test
  • Inception: Setting Teams Up for Success
    • Inception: What Is It and How Do We Do It?
    • How to Plan and Run an Inception
    • User Stories: Building Blocks of an MVP
      • User stories are vertically sliced
      • Slicing and dicing user stories
      • User story: The promise for a conversation
  • Product Delivery
    • Cadence of Delivery Activities
    • Measuring Product and Delivery
      • Delivery measures
      • Product measures
      • Model measures
      • Discovery measures
      • Commentary on measures
  • Conclusion
• II. Engineering
• 3. Effective Dependency Management: Principles and Tools
  • What If Our Code Worked Everywhere, Every Time?
    • A Better Way: Check Out and Go
    • Principles for Effective Dependency Management
      • Dependencies as code
      • Production-like development environments from day one
      • Application-level environment isolation
      • OS-level environment isolation
    • Tools for Dependency Management
      • Managing OS-level dependencies
        • Misconception 1: Docker is overcomplicated and unnecessary
        • Misconception 2: I dont need Docker because I already use X (e.g., conda)
        • Misconception 3: Docker will have a significant performance impact
        • Complicating the picture: Differing CPU chips and instruction sets
      • Managing application-level dependencies
  • A Crash Course on Docker and batect
    • What Are Containers?
    • Reduce the Number of Moving Parts in Docker with batect
      • Benefit 1: Simpler command-line interface
      • Benefit 2: Simple task composition
      • Benefit 3: Local-CI symmetry
      • Benefit 4: Faster builds with caches
      • How to use batect in your projects
  • Conclusion
• 4. Effective Dependency Management in Practice
  • In Context: ML Development Workflow
    • Identifying What to Containerize
    • Hands-On Exercise: Reproducible Development Environments, Aided by Containers
      • 1. Check out and go: Install prerequisite dependencies
      • 2. Create our local development environment
      • 3. Start our local development environment
      • 4. Serve the ML model locally as a web API
      • 5. Configure our code editor
      • 6. Train model on the cloud
      • 7. Deploy model web API
  • Secure Dependency Management
    • Remove Unnecessary Dependencies
    • Automate Checks for Security Vulnerabilities
  • Conclusion
• 5. Automated Testing: Move Fast Without Breaking Things
  • Automated Tests: The Foundation for Iterating Quickly and Reliably
    • Starting with Why: Benefits of Test Automation
    • If Automated Testing Is So Important, Why Arent We Doing It?
      • Reason 1: We think writing automated tests slows us down
      • Reason 2: We have CI/CD
      • Reason 3: We just dont know how to test ML systems
  • Building Blocks for a Comprehensive Test Strategy for ML Systems
    • The What: Identifying Components For Testing
      • Software logic
      • ML models
      • Putting it together: The ML Systems Test Pyramid
    • Characteristics of a Good Test and Pitfalls to Avoid
      • Tests should be independent and idempotent
      • Tests should fail fast and fail loudly
      • Tests should check behavior, not implementation
      • Tests should be runnable in your development environment
      • Tests must be part of feature development
      • Tests let us catch bugs once
    • The How: Structure of a Test
  • Software Tests
    • Unit Tests
      • How to design unit-testable code
      • How do I write a unit test?
    • Training Smoke Tests
      • How do I write these tests?
    • API Tests
      • How do I write these tests?
      • Recommended practice: Assert on the whole elephant
    • Post-deployment Tests
      • How do I write these tests?
  • Conclusion
• 6. Automated Testing: ML Model Tests
  • Model Tests
    • The Necessity of Model Tests
    • Challenges of Testing ML Models
    • Fitness Functions for ML Models
    • Model Metrics Tests (Global and Stratified)
      • How do I write these tests?
      • Advantages and limitations of metrics tests
    • Behavioral Tests
    • Testing Large Language Models: Why and How
      • Guidelines for designing an LLM test strategy
      • LLM testing techniques
        • Manual exploratory tests
        • Example-based tests
        • Benchmark tests
        • Property-based tests
        • LLM-based tests (aka auto-evaluator tests)
  • Essential Complementary Practices for Model Tests
    • Error Analysis and Visualization
    • Learn from Production by Closing the Data Collection Loop
    • Open-Closed Test Design
    • Exploratory Testing
    • Means to Improve the Model
    • Designing to Minimize the Cost of Failures
    • Monitoring in Production
    • Bringing It All Together
  • Next Steps: Applying What Youve Learned
    • Make Incremental Improvements
    • Demonstrate Value
  • Conclusion
• 7. Supercharging Your Code Editor with Simple Techniques
  • The Benefits (and Surprising Simplicity) of Knowing Our IDE
    • Why Should We Care About IDEs?
    • If IDEs Are So Important, Why Havent I Learned About Them Yet?
  • The Plan: Getting Productive in Two Stages
    • Stage 1: Configuring Your IDE
      • Install IDE and basic navigation shortcuts
      • Clone code repository
      • Create a virtual environment
      • Configure virtual environment: PyCharm
      • Configure virtual environment: VS Code
      • Testing that weve configured everything correctly
    • Stage 2: The Star of the ShowKeyboard Shortcuts
      • Coding
        • Code completion suggestions
        • Inline documentation / parameter information
        • Auto-fix suggestions
        • Linting
        • Move/copy lines
      • Formatting
        • Reformat code
      • Refactoring
        • Rename variable
        • Extract variable/method/function
      • Navigating code without getting lost
        • Opening things (files, classes, methods, functions) by name
        • Navigating the flow of code
        • Screen real estate management
    • You Did It!
      • Guidelines for setting up a code repository for your team
      • Additional tools and techniques
  • Conclusion
• 8. Refactoring and Technical Debt Management
  • Technical Debt: The Sand in Our Gears
    • Getting to a Healthy Level of Debt Through Tests, Design, and Refactoring
    • Refactoring 101
  • How to Refactor a Notebook (or a Problematic Codebase)
    • The Map: Planning Your Journey
    • The Journey: Hitting the Road
      • Step 1. Run the notebook or code and ensure it works as expected
      • Step 2. Remove print statements
      • Step 3. List code smells
      • Step 4. Convert the notebook to a Python file
      • Step 5. Adding characterization tests
      • Step 6. Refactor iteratively
        • The first refactoring: Remove dead code
        • The second refactoring: Abstract away implementation details
        • The third refactoring: Abstract away implementation details (again)
    • Looking Back at What Weve Achieved
      • Design principles that helped guide us
        • Separation of concerns
        • Open-closed design
        • Prefer obvious over obscure code (or explicit over implicit)
        • Reduce tight coupling (or couple to interfaces, not to implementation)
        • Simple design
  • Technical Debt Management in the Real World
    • Technical Debt Management Techniques
      • Make debt visible
      • The 80/20 rule
      • Make it cheap and safe
      • Demonstrate value of paying off technical debt
    • A Positive Lens on Debt: Systems Health Ratings
  • Conclusion: Make Good Easy
• 9. MLOps and Continuous Delivery for ML (CD4ML)
  • MLOps: Strengths and Missing Puzzle Pieces
    • MLOps 101
    • Smells: Hints That We Missed Something
      • MLOps smell 1: CI/CD pipelines with no tests
      • MLOps smell 2: Infrequent model deployments to production or preproduction environments
      • MLOps smell 3: Data in production goes to waste
      • MLOps smell 4: X is another teams responsibility
  • Continuous Delivery for Machine Learning
    • Benefits of CD4ML
    • A Crash Course on Continuous Delivery Principles
    • Building Blocks of CD4ML: Creating a Production-Ready ML System
      • Build quality into the product
        • Test automation
        • Shift left on security
      • Work in small batches
        • Practice pair programming
        • Use version control for all production artifacts
        • Implement continuous integration (CI)
        • Apply trunk-based development
      • Automation: Computers perform repetitive tasks, people solve problems
        • Automate development environment setup
        • Automate deployments (minimally to a preproduction environment)
        • Monitoring in production
      • Kaizen: Relentlessly pursue continuous improvement
      • Everyone is responsible: Rationalizing and cultivating ownership by adopting the appropriate team topologies
  • How CD4ML Supports ML Governance and Responsible AI
  • Conclusion
• III. Teams
• 10. Building Blocks of Effective ML Teams
  • Common Challenges Faced by ML Teams
  • Effective Team Internals
    • Trust as the Foundational Building Block
      • Daring Greatly
      • Tuckmans stages of group development
      • Belbins Team Roles
    • Communication
      • Models of communication
      • Crucial Conversations framework
      • Candor in feedback
    • Diverse Membership
      • Primary and secondary dimensions: Sociodemographic diversity
      • Tertiary dimensions: Functional and role diversity
    • Purposeful, Shared Progress
    • Internal Tactics to Build Effective Teams
  • Improving Flow with Engineering Effectiveness
    • Feedback Loops
    • Cognitive Load
    • Flow State
  • Conclusion
• 11. Effective ML Organizations
  • Common Challenges Faced by ML Organizations
  • Effective Organizations as Teams of Teams
    • The Role of Value-Driven Portfolio Management
    • Team Topologies Model
    • Team Topologies for ML Teams
      • Stream-aligned team: ML product team
      • Complicated subsystem team: ML domain team
      • Platform team: ML and data platform team
      • Enabling team: Specialists in some aspect of ML product development
      • Combining and evolving topologies
      • An example topology
      • Limitations of Team Topologies
    • Organizational Tactics to Build Effective Teams
  • Intentional Leadership
    • Create Structures and Systems for Effective Teams
    • Engage Stakeholders and Coordinate Organizational Resources
    • Cultivate Psychological Safety
    • Champion Continuous Improvement
    • Embrace Failure as a Learning Opportunity
    • Build the Culture We Wish We Had
    • Encourage Teams to Play at Work
  • Conclusion
    • Epilogue: Danas Journey
• Index

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