Wilson B.T. Machine Learning Engineering (MEAP)

New York: Manning Publications, 2020. — 123 p.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, 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!