## Using basic training APIs

Follow these sample notebooks to learn:

1. [Train within notebook](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-on-local): train a model using local computer as compute target.
3. [Train on remote VM](train-on-remote-vm): train a model using a remote Azure VM as compute target.
4. [Train on ML Compute](train-on-amlcompute): train a model using an ML Compute cluster as compute target.
5. [Train in an HDI Spark cluster](train-in-spark): train a Spark ML model using an HDInsight Spark cluster as compute target.
6. [Logging API](logging-api): experiment with various logging functions to create runs and automatically generate graphs.
7. [Manage runs](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-hyperparameter-tune-deploy-with-sklearn): train a model using the Scikit-learn estimator and tune hyperparameters with Hyperdrive.

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