Effective machine learning teams : best practices for ML practitioners

Effective machine learning teams best practices for ML practitioners

Gain the valuable skills and techniques you need to accelerate the delivery of machine learning solutions. With this practical guide, data scientists and ML engineers will learn how to bridge the gap between data science and Lean software delivery in a practical and simple way. David Tan and Ada Leu...

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Detalles Bibliográficos
Otros Autores: Tan, David (Machine Learning Engineer), author (author), Leung, Ada, author, Colls, David, author
Formato: Libro electrónico
Idioma:Inglés
Publicado: Sebastopol, CA : O'Reilly Media, Inc 2024.
Edición:First edition
Materias:
Machine learning.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009799019806719
Tabla de Contenidos:
  • Intro
  • Copyright
  • Table of Contents
  • 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
  • O'Reilly Online Learning
  • How to Contact Us
  • Acknowledgments
  • From David Tan
  • From Ada Leung
  • From David Colls
  • Chapter 1. Challenges and Better Paths in Delivering ML Solutions
  • ML: Promises and Disappointments
  • Continued Optimism in ML
  • Why ML Projects Fail
  • Is There a Better Way? How Systems Thinking and Lean Can Help
  • You Can't "MLOps" Your Problems Away
  • See the Whole: A Systems Thinking Lens for Effective ML Delivery
  • The Five Disciplines Required for Effective ML Delivery
  • Conclusion
  • Part I. Product and Delivery
  • Chapter 2. Product and Delivery Practices for ML Teams
  • ML Product Discovery
  • Discovering Product Opportunities
  • Canvases to Define Product Opportunities
  • Techniques for Rapidly Designing, Delivering, and Testing Solutions
  • 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
  • Product Delivery
  • Cadence of Delivery Activities
  • Measuring Product and Delivery
  • Conclusion
  • Part II. Engineering
  • Chapter 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
  • Tools for Dependency Management
  • A Crash Course on Docker and batect
  • What Are Containers?
  • Reduce the Number of Moving Parts in Docker with batect
  • Conclusion
  • Chapter 4. Effective Dependency Management in Practice
  • In Context: ML Development Workflow
  • Identifying What to Containerize
  • Hands-On Exercise: Reproducible Development Environments, Aided by Containers
  • Secure Dependency Management
  • Remove Unnecessary Dependencies
  • Automate Checks for Security Vulnerabilities
  • Conclusion
  • Chapter 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 Aren't We Doing It?
  • Building Blocks for a Comprehensive Test Strategy for ML Systems
  • The What: Identifying Components For Testing
  • Characteristics of a Good Test and Pitfalls to Avoid
  • The How: Structure of a Test
  • Software Tests
  • Unit Tests
  • Training Smoke Tests
  • API Tests
  • Post-deployment Tests
  • Conclusion
  • Chapter 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)
  • Behavioral Tests
  • Testing Large Language Models: Why and How

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