On-demand features - basic demo
This example trains and scores a model that uses an on-demand feature.
The feature parses a JSON string to extract a list of hover times on a webpage. These times are averaged together, and the mean is passed as a feature to a model.
Requirements:
- A cluster running Databricks Runtime for ML 13.3 LTS or above.
- The cluster access model must be Single user.
Helper functions and notebook variables
Setup
You can call the Python UDF from SQL, as shown in the next cell.
Create a TrainingSet with on-demand features
Log a simple model using the TrainingSet
For simplicity, this notebook uses a hard-coded model. In practice, you'll log a model trained on the generated TrainingSet.
Score the model using score_batch
Serve the Feature Store packaged model
Wait for the model serving endpoint to be ready.
Query the endpoint
Alternatively, use the Serving query endpoints UI to send a request:
{
"dataframe_records": [
{"json_blob": "{\"hover_time\": [5.5, 2.3, 10.3]}"}
]
}