Summary

In this chapter, we addressed some use cases, with example MLflow pipelines. We looked at implementing AutoML in two different scenarios. Where we don't have targets, we will need to use anomaly detection as an unsupervised ML technique. The use of non-Python-based platforms was addressed, and we concluded with how to extend MLflow with plugins.

At this stage, we have addressed a good breadth and depth of topics in the area of ML engineering using MLflow. Your next step is definitely to explore more, and leverage on your project the techniques learned in this book.