Exploring MLflow's components and usage patterns

Let's use the working example implemented in the previous section to explore the following central concepts, components, and usages in MLflow. These include experiments, runs, metadata about experiments, artifacts for experiments, models, and code.

Exploring experiments and runs in MLflow

Experiment is a first-class entity in the MLflow APIs. This makes sense as data scientists and ML engineers need to run lots of experiments in order to build a working model that meets the requirements. However, the idea of an experiment goes beyond just the model development stage and extends to the entire life cycle of the ML/DL development and deployment. So, this means that when we do retraining or training for a production version of the model, we need to treat them as production-quality experiments. This unified view of experiments builds a bridge between the offline and online production environments. Each experiment consists...