The typical machine learning workflow using Feature Store follows this path:
- Write code to convert raw data into features and create a Spark DataFrame containing the desired features.
- Write the DataFrame as a feature table in Feature Store.
- Create a training set based on features from feature tables.
- Train a model.
- Log the model as an MLflow model.
- Perform batch inference on new data. The model automatically retrieves the features it needs from Feature Store.