Machine Learning Engineering in Action you own this product

Ben Wilson
  • March 2022
  • ISBN 9781617298714
  • 576 pages
  • printed in black & white
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It’s like being advised by a seasoned professional every step of the way.

John Bassil, UNiDAYS
Look inside
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. He is also an MLflow committer.

FREE domestic shipping on orders of three or more print books

The ultimate resource for machine learning engineering.

Ninoslav Cerkez, Logit

A comprehensive roadmap for implementing ML across teams and in production.

Taylor Delehanty, Gaggle

Thorough guidance through all the steps for building a machine learning project. Full of valuable knowledge and experience.

Rui Liu, Oracle

Great for both junior and experienced professionals.

Ioannis Atsonios, Femtec Health
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