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@@ -34,26 +34,29 @@ Azure Machine Learning Pipelines optimize for simplicity, speed, and efficiency.
### Notebooks
In this directory, there are two types of notebooks:
* The first type of notebooks will introduce you to core Azure Machine Learning Pipelines features. These notebooks below belong in this category, and are designed to go in sequence; they're all located in the "intro-to-pipelines" folder:
**End-to-end introductory notebook series**
1. [aml-pipelines-getting-started.ipynb](https://aka.ms/pl-get-started): Start with this notebook to understand the concepts of using Azure Machine Learning Pipelines. This notebook will show you how to runs steps in parallel and in sequence.
2. [aml-pipelines-with-data-dependency-steps.ipynb](https://aka.ms/pl-data-dep): This notebooks shows how to connect steps in your pipeline using data. Data produced by one step is used by subsequent steps to force an explicit dependency between steps.
3. [aml-pipelines-publish-and-run-using-rest-endpoint.ipynb](https://aka.ms/pl-pub-rep): Once you are satisfied with your iterative runs in, you could publish your pipeline to get a REST endpoint which could be invoked from non-Pythons clients as well.
4. [aml-pipelines-data-transfer.ipynb](https://aka.ms/pl-data-trans): This notebook shows how you transfer data between supported datastores.
5. [aml-pipelines-use-databricks-as-compute-target.ipynb](https://aka.ms/pl-databricks): This notebooks shows how you can use Pipelines to send your compute payload to Azure Databricks.
6. [aml-pipelines-use-adla-as-compute-target.ipynb](https://aka.ms/pl-adla): This notebook shows how you can use Azure Data Lake Analytics (ADLA) as a compute target.
7. [aml-pipelines-how-to-use-estimatorstep.ipynb](https://aka.ms/pl-estimator): This notebook shows how to use the EstimatorStep.
7. [aml-pipelines-parameter-tuning-with-hyperdrive.ipynb](https://aka.ms/pl-hyperdrive): HyperDriveStep in Pipelines shows how you can do hyper parameter tuning using Pipelines.
8. [aml-pipelines-how-to-use-azurebatch-to-run-a-windows-executable.ipynb](https://aka.ms/pl-azbatch): AzureBatchStep can be used to run your custom code in AzureBatch cluster.
9. [aml-pipelines-setup-schedule-for-a-published-pipeline.ipynb](https://aka.ms/pl-schedule): Once you publish a Pipeline, you can schedule it to trigger based on an interval or on data change in a defined datastore.
10. [aml-pipelines-with-automated-machine-learning-step.ipynb](https://aka.ms/pl-automl): AutoMLStep in Pipelines shows how you can do automated machine learning using Pipelines.
Learn about Azure Machine Learning Pipelines by following the notebooks in this directory **in sequence**:
* The second type of notebooks illustrate more sophisticated scenarios, and are independent of each other. These notebooks include:
|Notebook|Description|
|--------|-----------|
|1. [aml-pipelines-getting-started.ipynb](https://aka.ms/pl-get-started)|Get started and run Azure Machine Learning Pipeline steps in parallel and in sequence.|
|2. [aml-pipelines-with-data-dependency-steps.ipynb](https://aka.ms/pl-data-dep)|Connect pipeline steps where data produced by one step is used by subsequent steps to force an explicit dependency between the steps. |
|3. [aml-pipelines-publish-and-run-using-rest-endpoint.ipynb](https://aka.ms/pl-pub-rep)|Publish pipelines to get a REST endpoint consumeable by Python and non-Pythons clients. |
|4. [aml-pipelines-data-transfer.ipynb](https://aka.ms/pl-data-trans)|Transfer data between supported datastores in pipelines.|
|5. [aml-pipelines-use-adla-as-compute-target.ipynb](https://aka.ms/pl-adla)|Run pipelines on Azure Data Lake Analytics (ADLA).|
|6. [aml-pipelines-how-to-use-estimatorstep.ipynb](https://aka.ms/pl-estimator)|Add estimator training to a pipeline with `EstimatorStep`.|
|7. [aml-pipelines-parameter-tuning-with-hyperdrive.ipynb](https://aka.ms/pl-hyperdrive)|Hyperparameter tune in your pipelines with `HyperDriveStep`.|
|8. [aml-pipelines-how-to-use-azurebatch-to-run-a-windows-executable.ipynb](https://aka.ms/pl-azbatch)|Run custom code in an Azure Batch cluster with `AzureBatchStep`.|
|9. [aml-pipelines-setup-schedule-for-a-published-pipeline.ipynb](https://aka.ms/pl-schedule)|Schedule published pipeline job at specific intervals or after change in datastore.|
|10. [aml-pipelines-with-automated-machine-learning-step.ipynb](https://aka.ms/pl-automl)|Use automated ML in your pipelines with `AutoMLStep`.|
1. [pipeline-batch-scoring.ipynb](https://aka.ms/pl-batch-score): This notebook demonstrates how to run a batch scoring job using Azure Machine Learning pipelines.
2. [pipeline-style-transfer.ipynb](https://aka.ms/pl-style-trans)
**Advanced scenarios**
|Notebook|Description|
|--------|-----------|
|[pipeline-batch-scoring.ipynb](https://aka.ms/pl-batch-score)|Run a batch scoring job using Azure Machine Learning pipelines|
|[pipeline-style-transfer.ipynb](https://aka.ms/pl-style-trans)||
![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/README.png)