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Removed databricks notebook link

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Jeff Shepherd
2019-01-02 11:45:52 -08:00
parent 5fe5f06e07
commit c1aa951867
1 changed files with 1 additions and 5 deletions
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6
how-to-use-azureml/automated-machine-learning/README.md
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@@ -35,7 +35,7 @@ Below are the three execution environments supported by AutoML.
**NOTE**: You should at least have contributor access to your Azure subcription to run the notebook.
- Please remove the previous SDK version if there is any and install the latest SDK by installing **azureml-sdk[automl_databricks]** as a PyPi library in Azure Databricks workspace.
- You can find the detail Readme instructions at [GitHub](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/azure-databricks).
- Download the sample notebook AutoML_Databricks_local_06.ipynb from [GitHub](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/azure-databricks) and import into the Azure databricks workspace.
- Download the sample notebook automl-databricks-local-01.ipynb from [GitHub](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/azure-databricks) and import into the Azure databricks workspace.
- Attach the notebook to the cluster.
<a name="localconda"></a>
@@ -175,10 +175,6 @@ bash automl_setup_linux.sh
- [auto-ml-dataprep-remote-execution.ipynb](dataprep-remote-execution/auto-ml-dataprep-remote-execution.ipynb)
- Using DataPrep for reading data with remote execution
- [auto-ml-classification-local-azuredatabricks.ipynb](classification-local-azuredatabricks/auto-ml-classification-local-azuredatabricks.ipynb)
- Dataset: scikit learn's [digit dataset](https://innovate.burningman.org/datasets-page/)
- Example of using AutoML for classification using Azure Databricks as the platform for training
- [auto-ml-classification-with-whitelisting.ipynb](classification-with-whitelisting/auto-ml-classification-with-whitelisting.ipynb)
- Dataset: scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits)
- Simple example of using Auto ML for classification with whitelisting tensorflow models.