jprdonnelly/MachineLearningNotebooks
1
0
Fork 0
mirror of https://github.com/Azure/MachineLearningNotebooks.git synced 2025-12-22 10:35:12 -05:00
Code Issues Projects Releases Wiki Activity

Update README.md

Browse Source
This commit is contained in:
Josée Martens
2019-05-08 12:25:41 -05:00
committed by GitHub
parent dcce6f227f
commit 83cfe3b9b3
1 changed files with 19 additions and 16 deletions
Download Patch File Download Diff File Expand all files Collapse all files

35
how-to-use-azureml/machine-learning-pipelines/README.md
View File

@@ -34,26 +34,29 @@ Azure Machine Learning Pipelines optimize for simplicity, speed, and efficiency.
### Notebooks ### 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. Learn about Azure Machine Learning Pipelines by following the notebooks in this directory **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.
* 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. **Advanced scenarios**
2. [pipeline-style-transfer.ipynb](https://aka.ms/pl-style-trans)
|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) ![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/machine-learning-pipelines/README.png)