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. YouGÇÖll adopt software development standards thatdeliver better code management, and make it easier to test, scale, and evenreuse your machine learning code!