Machine Learning Engineering in Action

Field-tested tips, tricks, and design patterns for building MachineLearning projects that are deployable, maintainable, and secure from concept toproduction.In Machine Learning Engineering inAction, you will learn:          Evaluatingdata science problems to find the most effective solution       Scopinga machine learning project for usage expectations and budget       Processtechniques that minimize wasted effort and speed up production       Assessinga project using standardized prototyping work and statistical validation       Choosingthe right technologies and tools for your project       Makingyour codebase more understandable, maintainable, and testable       Automatingyour troubleshooting and logging practices Databricks solutions architect BenWilson lays out an approach to building deployable, maintainable productionmachine learning systems. You''ll adopt software development standards thatdeliver better code management, and make it easier to test, scale, and evenreuse your machine learning code!