Machine Learning Engineering in Action lays out an approach to building deployable, maintainable production machine learning systems. You will adopt software development standards that deliver better code management, and make it easier to test, scale, and even reuse your machine learning code!
You will 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 will 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 will be able to reliably deliver cost-effective solutions for organizations big and small alike.
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.