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Completes feature engineering scenario 1 example
This example shows how one might use Hamilton to compute features in an offline and online fashion. The assumption here is that the request passed into the API has all the raw data required to compute features. This example also shows how one might "override" some values that are required for computing features, in this example they are `age_mean` and `age_std_dev`. This can be required when you computing aggregation features does not make sense at inference time.
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fastapi | ||
pandas | ||
pandera | ||
sf-hamilton | ||
uvicorn |
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STAY TUNED...! |