Summary

In this chapter, we introduced the concepts and different features in terms of using MLflow to create production training processes.

We started by setting up the basic blocks of the MLflow training project and followed along throughout the chapter to, in sequence, train a model, evaluate a trained model, and register a trained model. We also delved into the creation of a ready-to-use image for your training job.

This was an important component of the architecture, and it will allow us to build an end-to-end production system for our ML system in production. In the next chapter, we will deploy different components and illustrate the deployment process of models.