Summary

This chapter has covered how to apply a lot of the techniques learned in this book, in particular from Chapter 2, The Machine Learning Development Process, Chapter 3, From Model to Model Factory, Chapter 4, Packaging Up, and Chapter 5, Deployment Patterns and Tools, to a realistic application scenario. The problem, in this case, concerned clustering taxi rides to find anomalous rides and then performing NLP on some contextual text data to try and help explain those anomalies automatically. This problem was tackled using the ETML pattern, which I offered up as a way to rationalize typical batch ML engineering solutions. This was explained in detail. A design for a potential solution, as well as a discussion of some of the tooling choices any ML engineering team would have to go through, was covered. Finally, a deep dive into some of the key pieces of work that would be required to make this solution production-ready was performed. In particular we showed how you can use good...