Chapter 3: Code Meets Data

In this chapter, we'll get started with hands-on MLOps implementation as we learn by solving a business problem using the MLOps workflow discussed in the previous chapter. We'll also discuss effective methods of source code management for machine learning (ML), explore data quality characteristics, and analyze and shape data for an ML solution.

We begin this chapter by categorizing the business problem to curate a best-fit MLOps solution for it. Following this, we'll set up the required resources and tools to implement the solution. 10 guiding principles for source code management for ML are discussed to apply clean code practices. We will discuss what constitutes good-quality data for ML and much more, followed by processing a dataset related to the business problem and ingesting and versioning it to the ML workspace. Most of the chapter is hands-on and designed to equip you with a good understanding of and experience with MLOps. For this...