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In previous chapters, we introduced a lot of the tools and techniques you will need to use to successfully build working Machine Learning (ML) products. We also introduced a lot of example pieces of code that helped us to understand how to implement these tools and techniques. So far, this has all been about what we need to program, but this chapter will focus on how to program. In particular, we will introduce and work with a lot of the techniques, methodologies, and standards that are prevalent in the wider Python software development community and apply them to ML use cases. The conversation will be centered around the concept of developing user-defined libraries and packages, reusable pieces of code that you can use for deploying your ML solutions or for developing new ones. It is important to note that everything we discuss here can be applied to all of your Python development activities across your ML...