Machine Learning Engineering in Action

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

Machine Learning Engineering in Action 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!

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.