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33 lines
3.1 KiB
Markdown
33 lines
3.1 KiB
Markdown
Azure Databricks is a managed Spark offering on Azure and customers already use it for advanced analytics. It provides a collaborative Notebook based environment with CPU or GPU based compute cluster.
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In this section, you will find sample notebooks on how to use Azure Machine Learning SDK with Azure Databricks. You can train a model using Spark MLlib and then deploy the model to ACI/AKS from within Azure Databricks. You can also use Automated ML capability (**public preview**) of Azure ML SDK with Azure Databricks.
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- Customers who use Azure Databricks for advanced analytics can now use the same cluster to run experiments with or without automated machine learning.
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- You can keep the data within the same cluster.
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- You can leverage the local worker nodes with autoscale and auto termination capabilities.
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- You can use multiple cores of your Azure Databricks cluster to perform simultenous training.
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- You can further tune the model generated by automated machine learning if you chose to.
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- Every run (including the best run) is available as a pipeline, which you can tune further if needed.
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- The model trained using Azure Databricks can be registered in Azure ML SDK workspace and then deployed to Azure managed compute (ACI or AKS) using the Azure Machine learning SDK.
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Please follow our [Azure doc](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-environment#azure-databricks) to install the sdk in your Azure Databricks cluster before trying any of the sample notebooks.
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**Single file** -
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The following archive contains all the sample notebooks. You can the run notebooks after importing [DBC](Databricks_AMLSDK_1-4_6.dbc) in your Databricks workspace instead of downloading individually.
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Notebooks 1-4 have to be run sequentially & are related to Income prediction experiment based on this [dataset](https://archive.ics.uci.edu/ml/datasets/adult) and demonstrate how to data prep, train and operationalize a Spark ML model with Azure ML Python SDK from within Azure Databricks.
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Notebook 6 is an Automated ML sample notebook for Classification.
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Learn more about [how to use Azure Databricks as a development environment](https://docs.microsoft.com/azure/machine-learning/service/how-to-configure-environment#azure-databricks) for Azure Machine Learning service.
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**Databricks as a Compute Target from AML Pipelines**
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You can use Azure Databricks as a compute target from [Azure Machine Learning Pipelines](https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-ml-pipelines). Take a look at this notebook for details: [aml-pipelines-use-databricks-as-compute-target.ipynb](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/azure-databricks/databricks-as-remote-compute-target/aml-pipelines-use-databricks-as-compute-target.ipynb).
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For more on SDK concepts, please refer to [notebooks](https://github.com/Azure/MachineLearningNotebooks).
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**Please let us know your feedback.**
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