Chapter 2: The Machine Learning Development Process

In this chapter, we will define how the work for any successful Machine Learning (ML) software engineering project can be divided up. Basically, we will answer the question of how do you actually organize the doing of a successful ML project? We will not only discuss the process and workflow, but we will also set up the tools you will need for each stage of the process and highlight some important best practices with real ML code examples.

Specifically, this chapter will cover the concept of a discover, play, develop, deploy workflow for your ML projects, appropriate development tooling and their configuration and integration for a successful project. We will also cover version control strategies and their basic implementation, setting up Continuous Integration/Continuous Deployment (CI/CD) for your ML project. We will also introduce some potential execution environments. At the end of this chapter, you will be set up for success...