Effective machine learning teams : best practices for ML practitioners / David Tan, Ada Leung, and David Colls.

Format:

Book

Author/Creator:

Tan, David (Machine Learning Engineer), author.

Leung, Ada, author.

Colls, David, author.

Language:

English

Subjects (All):

Machine learning.

Physical Description:

1 online resource : illustrations

Edition:

First edition.

Place of Publication:

Sebastopol, CA : O'Reilly Media, Inc., 2024.

Summary:

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 Leung from Thoughtworks show you how to apply time-tested software engineering skills and Lean delivery practices that will improve your effectiveness in ML projects. Based on the authors' experience across multiple real-world data and ML projects, the proven techniques in this book will help teams avoid common traps in the ML world, so you can iterate more quickly and reliably. With these techniques, data scientists and ML engineers can overcome friction and experience flow when delivering machine learning solutions. This book shows you how to: Apply engineering practices such as writing automated tests, containerizing development environments, and refactoring problematic code bases Apply MLOps and CI/CD practices to accelerate experimentation cycles and improve reliability of ML solutions Design maintainable and evolvable ML solutions that allow you to respond to changes in an agile fashion Apply delivery and product practices to iteratively improve your odds of building the right product for your users Use intelligent code editor features to code more effectively.

Contents:

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

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

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

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

Notes:

OCLC-licensed vendor bibliographic record.

ISBN:

9781098144623

1098144627

OCLC:

1420046764