
## Follow these sample notebooks to learn:

1. [Logging API](./logging-api/logging-api.ipynb): experiment with various logging functions to create runs and automatically generate graphs.
2. [Manage runs](./manage-runs/manage-runs.ipynb): learn different ways how to start runs and child runs, monitor them, and cancel them.
1. [Tensorboard to monitor runs](./tensorboard/tensorboard.ipynb)

## Use MLflow with Azure Machine Learning service (Preview)

[MLflow](https://mlflow.org/) is an open-source platform for tracking machine learning experiments and managing models. You can use MLflow logging APIs with Azure Machine Learning service: the metrics and artifacts are logged to your Azure ML Workspace.

Try out the sample notebooks:
1. [Use MLflow with Azure Machine Learning for Local Training Run](./train-local/train-local.ipynb)
1. [Use MLflow with Azure Machine Learning for Remote Training Run](./train-remote/train-remote.ipynb)
1. [Deploy Model as Azure Machine Learning Web Service using MLflow](./deploy-model/deploy-model.ipynb)
1. [Train and Deploy PyTorch Image Classifier](./train-deploy-pytorch/train-deploy-pytorch.ipynb)

 ![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/track-and-monitor-experiments/README.png)

