MLflow tutorials using example Jupyter notebooks
The following tutorials demonstrate how to integrate MLflow experiments into your training workflows. To clean up resources created by a notebook tutorial, see Clean up MLflow resources.
You can run SageMaker example notebooks using JupyterLab in Studio. For more information on JupyterLab, see JupyterLab user guide.
Explore the following example notebooks:
-
SageMaker Training with MLflow
— Train and register a Scikit-Learn model using SageMaker in script mode. Learn how to integrate MLflow experiments into your training script. For more information on model training, see Train a Model with Amazon SageMaker. -
SageMaker HPO with MLflow
— Learn how to track your ML experiment in MLflow with Amazon SageMaker automatic model tuning (AMT) and the SageMaker Python SDK. Each training iteration is logged as a run within the same experiment. For more information about hyperparameter optimization (HPO), see Perform Automatic Model Tuning with Amazon SageMaker. -
SageMaker Pipelines with MLflow
— Use Amazon SageMaker Pipelines and MLflow to train, evaluate and register a model. This notebook uses the @stepdecorator to build a SageMaker Pipeline. For more information on pipelines and the@stepdecorator, see Create a pipeline with@step-decorated functions. -
Deploy an MLflow Model to SageMaker
— Train a decision tree model using SciKit-Learn. Then, use Amazon SageMaker ModelBuilderto deploy the model to a SageMaker endpoint and run inference using the deployed model. For more information aboutModelBuilder, see Deploy MLflow models with ModelBuilder.
