Machine Learning Engineering in Action, Ben Wilson

Field-tested tips, tricks, and design patterns for building machine learning projects that are deployable, maintainable, and secure from concept to production.

In

Machine Learning Engineering in Action

, you will learn:

Evaluating data science problems to find the most effective solution

Scoping a machine learning project for usage expectations and budget

Process techniques that minimize wasted effort and speed up production

Assessing a project using standardized prototyping work and statistical validation

Choosing the right technologies and tools for your project

Making your codebase more understandable, maintainable, and testable

Automating your troubleshooting and logging practices

Ferrying a machine learning project from your data science team to your end users is no easy task.

Machine Learning Engineering in Action

will help you make it simple. Inside, you’ll find fantastic advice from veteran industry expert Ben Wilson, Principal Resident Solutions Architect at Databricks.

Ben introduces his personal toolbox of techniques for building deployable and maintainable production machine learning systems. You’ll learn the importance of Agile methodologies for fast prototyping and conferring with stakeholders, while developing a new appreciation for the importance of planning. Adopting well-established software development standards will help you deliver better code management, and make it easier to test, scale, and even reuse your machine learning code. Every method is explained in a friendly, peer-to-peer style and illustrated with production-ready source code.

About the technology

Deliver maximum performance from your models and data. This collection of reproducible techniques will help you build stable data pipelines, efficient application workflows, and maintainable models every time. Based on decades of good software engineering practice, machine learning engineering ensures your ML systems are resilient, adaptable, and perform in production.

About the book

Machine Learning Engineering in Action

teaches you core principles and practices for designing, building, and delivering successful machine learning projects. You’ll discover software engineering techniques like conducting experiments on your prototypes and implementing modular design that result in resilient architectures and consistent cross-team communication. Based on the author’s extensive experience, every method in this book has been used to solve real-world projects.

What's inside

Scoping a machine learning project for usage expectations and budget

Choosing the right technologies for your design

Making your codebase more understandable, maintainable, and testable

Automating your troubleshooting and logging practices

About the reader

For data scientists who know machine learning and the basics of object-oriented programming.

About the author

Ben Wilson

is Principal Resident Solutions Architect at Databricks, where he developed the Databricks Labs AutoML project, and is an MLflow committer.

Table of Contents

PART 1 AN INTRODUCTION TO MACHINE LEARNING ENGINEERING

1 What is a machine learning engineer?

2 Your data science could use some engineering

3 Before you model: Planning and scoping a project

4 Before you model: Communication and logistics of projects

5 Experimentation in action: Planning and researching an ML project

6 Experimentation in action: Testing and evaluating a project

7 Experimentation in action: Moving from prototype to MVP

8 Experimentation in action: Finalizing an MVP with MLflow and runtime optimization

PART 2 PREPARING FOR PRODUCTION: CREATING MAINTAINABLE ML

9 Modularity for ML: Writing testable and legible code

10 Standards of coding and creating maintainable ML code

11 Model measurement and why it’s so important

12 Holding on to your gains by watching for drift

13 ML development hubris

PART 3 DEVELOPING PRODUCTION MACHINE LEARNING CODE

14 Writing production code

15 Quality and acceptance testing

16 Production infrastructure

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