MLflow Quick Start: Model Training and Logging

This notebook is part 1 of a Quick Start guide based on the MLflow tutorial. The following MLflow Quick Start notebook shows how to:

  • Install MLflow on a Databricks cluster and an MLflow tracking server on a Linux instance
  • Connect the notebook to the tracking server
  • Train a model and log the training metrics, parameters, and model artifacts to the tracking server
  • View the training results in the MLflow tracking server UI

To learn how to deploy the trained model on AWS SageMaker, see part 2, MLflow: Model Deployment and Prediction.