MLflow Quick Start: Model Deployment and Prediction

This notebook is part 2 of a Quick Start guide based on the MLflow tutorial. As in part 1, MLflow Quick Start: Model Training and Logging, this notebook uses ElasticNet models trained on the diabetes dataset in scikit-learn. This part of the tutorial shows how to:

  • Select a model to deploy using the MLflow tracking UI
  • Deploy the model to SageMaker using the MLflow API
  • Query the deployed model using the sagemaker-runtime API
  • Repeat the deployment and query process for another model
  • Delete the deployment using the MLflow API

For information on how to configure AWS authentication so that you can deploy MLflow models in AWS SageMaker from Databricks, see Set up AWS Authentication for SageMaker Deployment.