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 AI example notebooks using JupyterLab in Studio. For more information on JupyterLab, see JupyterLab user guide.
Explore the following example notebooks:
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SageMaker Training with MLflow
— Train and register a Scikit-Learn model using SageMaker AI 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 AI. -
SageMaker AI HPO with MLflow
— Learn how to track your ML experiment in MLflow with Amazon SageMaker AI automatic model tuning (AMT) and the SageMaker AI 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 AI. -
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 AI Pipeline. For more information on pipelines and the@stepdecorator, see Create a pipeline with@step-decorated functions. -
Deploy an MLflow Model to SageMaker AI
— Train a decision tree model using SciKit-Learn. Then, use Amazon SageMaker AI ModelBuilderto deploy the model to a SageMaker AI endpoint and run inference using the deployed model. For more information aboutModelBuilder, see Deploy MLflow models with ModelBuilder.
