MachineLearningNotebooks/how-to-use-azureml/training/README.md

Using basic training APIs

Follow these sample notebooks to learn:

1. Train within notebook: train a simple scikit-learn model using the Jupyter kernel and deploy the model to Azure Container Service.
2. Train on local: train a model using local computer as compute target.
3. Train on remote VM: train a model using a remote Azure VM as compute target.
4. Train on ML Compute: train a model using an ML Compute cluster as compute target.
5. Train in an HDI Spark cluster: train a Spark ML model using an HDInsight Spark cluster as compute target.
6. Logging API: experiment with various logging functions to create runs and automatically generate graphs.
7. Manage runs: learn different ways how to start runs and child runs, monitor them, and cancel them.
8. Train and hyperparameter tune on Iris Dataset with Scikit-learn: train a model using the Scikit-learn estimator and tune hyperparameters with Hyperdrive.