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 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.

In this edition, there will be more details on an important data science and ML project management methodology: Cross-Industry Standard Process for Data Mining (CRISP-DM). This will include a discussion of how this methodology compares to traditional Agile and Waterfall ...

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