This notebook uses ElasticNet models trained on the diabetes dataset described in Track scikit-learn model training with MLflow. The notebook shows how to:
Select a model to deploy using the MLflow experiment UI
Deploy the model to SageMaker using the MLflow API
Query the deployed model using the
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.
If you prefer to serve your registered model using Databricks, see Model serving with Databricks.