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Machine Learning Engineering in Action you own this product

Ben Wilson
  • MEAP began December 2020
  • Publication in April 2022 (estimated)
  • ISBN 9781617298714
  • 300 pages (estimated)
  • printed in black & white
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A nice view on practical data science and machine learning.Great reading for newbies, some interesting views for seasoned practitioners.

Johannes Verwijnen
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

Databricks solutions architect Ben Wilson lays out an approach to building deployable, maintainable production machine learning systems. You’ll adopt software development standards that deliver better code management, and make it easier to test, scale, and even reuse your machine learning code!

about the technology

Following established processes and methodology maximizes the likelihood that your machine learning projects will survive and succeed for the long haul. By adopting standard, reproducible practices, your projects will be maintainable over time and easy for new team members to understand and adapt.

about the book

Machine Learning Engineering in Action is a roadmap to delivering successful machine learning projects. It teaches you to adopt an efficient, sustainable, and goal-driven approach that author Ben Wilson has developed over a decade of data science experience. Every method in this book has been used to solve a breakdown in a real-world project, and is illustrated with production-ready source code and easily reproducible examples.

You’ll learn how to plan and scope your project, manage cross-team logistics that avoid fatal communication failures, and design your code’s architecture for improved resilience. You’ll even discover when not to use machine learning—and the alternative approaches that might be cheaper and more effective. When you’re done working through this toolbox guide, you’ll be able to reliably deliver cost-effective solutions for organizations big and small alike.

about the reader

For data scientists familiar with supervised machine learning and the basics of object-oriented programming.

about the author

Ben Wilson has worked as a professional data scientist for more than ten years. He currently works as a resident solutions architect at Databricks, where he focuses on machine learning production architecture with companies ranging from 5-person startups to global Fortune 100. Ben is the creator and lead developer of the Databricks Labs AutoML project, a Scala-and Python-based toolkit that simplifies machine learning feature engineering, model tuning, and pipeline-enabled modeling.

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A must read for those looking to balance the planning and experimentation lifecycle.

Jesús Antonino Juárez Guerrero

A practical book to help engineers understand the workflow of machine learning projects.

Xiangbo Mao

Do not implement your ML model into production without reading this book!

Lokesh Kumar