MLflow Tracking

MLflow Tracking allows you to track the training of your ML models. It also improves the observability of the model-training process. The MLflow Tracking feature allows you to log the generated metrics, artifacts, and the model itself as part of the model training process. MLflow Tracking also keeps track of model lineage in the Databricks environment. In Databricks, we can see the exact version of the notebook responsible for generating the model listed as the source.

MLflow also provides automatic logging (autolog) capabilities that automatically log many metrics, parameters, and artifacts while performing model training. We can also add our own set of metrics and artifacts to the log.

Using MLflow Tracking, we can chronologically track model training. Certain terms are specific to MLflow Tracking. Let’s take a look at them:

• Experiments: Training and tuning the ML model for a business problem is an experiment. By default, each Python notebook...