Summary

In this chapter, we introduced the experiments component of MLflow. We got to understand the logging metrics and artifacts in MLflow. We detailed the steps to track experiments in MLflow.

In the final sections, we explored the use case of hyperparameter optimization using the concepts learned in the chapter.

In the next chapter, we will focus on managing models with MLflow using the models developed in this chapter.