Summary
This chapter was all about building a solid foundation for future work. We discussed the development steps common to all ML engineering projects, which we called discover, play, develop, deploy. In particular, we outlined the aim of each of these steps and their desired outputs.
This was followed by a high-level discussion of tooling, and a walkthrough of the main setup steps. We set up the tools for developing our code, keeping track of the changes of that code, managing our ML engineering project, and finally, deploying our solutions.
In the rest of the chapter, we went through the details for each of the four stages we outlined previously, with a particular focus on the develop and deploy stages. Our discussion covered everything from the pros and cons of Waterfall and Agile development methodologies to environment management and then software development best practices. We also discussed how to apply testing to our ML code. We finished off with an exploration of how...
