Field-tested tips, tricks, and design patterns for building Machine Learning projects that are deployable, maintainable, and secure from concept to production.
A nice view on practical data science and machine learning.Great reading for newbies, some interesting views for seasoned practitioners.
In Machine Learning Engineering in Action
, 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!