Track scikit-learn model training with MLflow
This notebook is based on the MLflow tutorial.
The notebook shows how to use MLflow to track the model training process, including logging model parameters, metrics, the model itself, and other artifacts like plots to a Databricks hosted tracking server. It also includes instructions for viewing the logged results in the MLflow tracking UI.
To learn how to deploy the trained model on AWS SageMaker, see scikit-learn model deployment on SageMaker.