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This notebook illustrates the use of Databricks Feature Store to publish features to an online store for real-time
serving and automated feature lookup. The problem is to predict the wine quality using a ML model
with a variety of static wine features and a realtime input.
This notebook creates an endpoint to predict the quality of a bottle of wine, given an ID and the realtime feature alcohol by volume (ABV).
The notebook is structured as follows:
1. Prepare the feature table
2. Set up DynamoDB
* This notebook uses DynamoDB. For a list of supported online stores, see the Databricks documentation ([AWS](https://docs.databricks.com/applications/machine-learning/feature-store/online-feature-stores.html)|[Azure](https://docs.microsoft.com/azure/databricks/applications/machine-learning/feature-store/online-feature-stores)).
3. Publish the features to online feature store
4. Train and deploy the model
5. Serve realtime queries with automatic feature lookup
6. Clean up
This example uses the [Wine Quality Data Set](https://archive.ics.uci.edu/ml/datasets/wine+quality).
* Databricks Runtime 10.4 LTS for Machine Learning or above
* Access to AWS DynamoDB
- This notebook uses DynamoDB as the online store and guides you through how to generate secrets and register them with Databricks
Secret Management.
Online Feature Store example notebook
This notebook illustrates the use of Databricks Feature Store to publish features to an online store for real-time serving and automated feature lookup. The problem is to predict the wine quality using a ML model with a variety of static wine features and a realtime input.
This notebook creates an endpoint to predict the quality of a bottle of wine, given an ID and the realtime feature alcohol by volume (ABV).
The notebook is structured as follows:
Data Set
This example uses the Wine Quality Data Set.
Requirements