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@ -14,7 +14,7 @@ The AcceleratedML framework tries to solve this problem at scale as well as simp
2. Auto Tune Model Notebooks **(Master, Trigger notebooks)**
3. Azure ML Services workspace
4. Python cluster in Databricks with configurations as mentioned in Installations link above (PyPi library azureml-sdk[automl],azureml-opendatasets, azureml-widgets in cluster)
5. For sample to be used in notebook- [Real Estate Data](http://archive.ics.uci.edu/ml/datasets/Real+estate+valuation+data+set![image](https://user-images.githubusercontent.com/52777362/128715027-7d636c8c-7e9e-4e9d-a4a3-12284079f76e.png)
5. For sample to be used in notebook refer- [Real Estate Data](https://archive.ics.uci.edu/ml/datasets/Real+estate+valuation+data+set)
)
## How to use it