MLflow tutorials using example Jupyter 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 Model Building Pipelines and MLflow to train, evaluate and register a model. This notebook uses the @step decorator to build a SageMaker Pipeline. For more information on pipelines and the @step decorator, 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 ModelBuilder to deploy the model to a SageMaker endpoint and run inference using the deployed model. For more information about ModelBuilder, see Deploy MLflow models with ModelBuilder.