Ordering MLflow steps

In order to succeed in the modeling and evaluation phases of the ML lifecycle, you need to ensure that you keep an organized workspace, recording a history of various experiments to ensure cross-run comparability and reproducibility. MLflow provides a helpful, comprehensive platform to manage experiments robustly. In the video, you learned the various steps and commands used to create, start, log to, and retrieve runs. In this exercise, you will order the MLflow commands generally used in experiment management.