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@@ -4,8 +4,8 @@ Learn how to use Azure Machine Learning services for experimentation and model m
As a pre-requisite, run the [configuration Notebook](../configuration.ipynb) notebook first to set up your Azure ML Workspace. Then, run the notebooks in following recommended order. As a pre-requisite, run the [configuration Notebook](../configuration.ipynb) notebook first to set up your Azure ML Workspace. Then, run the notebooks in following recommended order.
* [train-within-notebook](./training/train-within-notebook): Train a model hile tracking run history, and learn how to deploy the model as web service to Azure Container Instance. * [train-within-notebook](./training/train-within-notebook): Train a model hile tracking run history, and learn how to deploy the model as web service to Azure Container Instance.
* [train-on-local](./training/train-on-local): Learn how to submit a run and use Azure ML managed run configuration. * [train-on-local](./training/train-on-local): Learn how to submit a run and use Azure ML managed run configuration.
* [train-on-amlcompute](./training/train-on-amlcompute): Use a 1-n node managed compute cluster as a remote compute target for CPU or GPU based training. * [train-on-amlcompute](./training/train-on-amlcompute): Use a 1-n node managed compute cluster as a remote compute target for CPU or GPU based training.
* [train-on-remote-vm](./training/train-on-remote-vm): Use Data Science Virtual Machine as a target for remote runs. * [train-on-remote-vm](./training/train-on-remote-vm): Use Data Science Virtual Machine as a target for remote runs.
* [logging-api](./training/logging-api): Learn about the details of logging metrics to run history. * [logging-api](./training/logging-api): Learn about the details of logging metrics to run history.