# MLflow tutorials using example Jupyter notebooks
<a name="mlflow-tutorials"></a>

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](mlflow-cleanup.md). 

You can run SageMaker AI example notebooks using JupyterLab in Studio. For more information on JupyterLab, see [JupyterLab user guide](studio-updated-jl-user-guide.md).

Explore the following example notebooks:
+ [SageMaker Training with MLflow](https://sagemaker-examples.readthedocs.io/en/latest/sagemaker-mlflow/sagemaker_training_mlflow.html) — 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](https://docs.aws.amazon.com/sagemaker/latest/dg/how-it-works-training.html).
+ [SageMaker AI HPO with MLflow](https://sagemaker-examples.readthedocs.io/en/latest/sagemaker-mlflow/sagemaker_hpo_mlflow.html) — 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](https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning.html).
+ [SageMaker Pipelines with MLflow](https://sagemaker-examples.readthedocs.io/en/latest/sagemaker-mlflow/sagemaker_pipelines_mlflow.html) — Use Amazon SageMaker Pipelines and MLflow to train, evaluate and register a model. This notebook uses the `@step` decorator to build a SageMaker AI Pipeline. For more information on pipelines and the `@step` decorator, see [Create a pipeline with `@step`-decorated functions](https://docs.aws.amazon.com/sagemaker/latest/dg/pipelines-step-decorator-create-pipeline.html).
+ [Deploy an MLflow Model to SageMaker AI](https://sagemaker-examples.readthedocs.io/en/latest/sagemaker-mlflow/sagemaker_deployment_mlflow.html) — Train a decision tree model using SciKit-Learn. Then, use Amazon SageMaker AI `ModelBuilder` to deploy the model to a SageMaker AI endpoint and run inference using the deployed model. For more information about `ModelBuilder`, see [Deploy MLflow models with `ModelBuilder`](mlflow-track-experiments-model-deployment.md).