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feat: Adding documentation for On Demand Feature Transformations with writes #4607

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franciscojavierarceo merged 4 commits into from
Oct 10, 2024
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feat: Adding documentation for On Demand Feature Transformations with writes #4607

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Update beta-on-demand-feature-view.md
franciscojavierarceo Oct 2, 2024
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updated docs
franciscojavierarceo Oct 7, 2024
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feat: Adding documentation for On Demand Feature Transformations with…
franciscojavierarceo Oct 7, 2024
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Merge branch 'master' into odfv-doc-updates
franciscojavierarceo Oct 7, 2024
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Update beta-on-demand-feature-view.md
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franciscojavierarceo committed Oct 7, 2024
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4 changes: 2 additions & 2 deletions docs/reference/beta-on-demand-feature-view.md
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On demand feature views allows data scientists to use existing features and request time data (features only available at request time) to transform and create new features. Users define python transformation logic which is executed in both historical retrieval and online retrieval paths.

Currently, these transformations are executed locally. This is fine for online serving, but does not scale well offline.
Currently, these transformations are executed on reads and locally. This is fine for online serving, but does not scale well offline.

### Why use on demand feature views?

This enables data scientists to easily impact the online feature retrieval path. For example, a data scientist could

1. Call `get_historical_features` to generate a training dataframe
2. Iterate in notebook on feature engineering in Pandas
2. Iterate in notebook on feature engineering in Pandas/Python
3. Copy transformation logic into on demand feature views and commit to a dev branch of the feature repository
4. Verify with `get_historical_features` (on a small dataset) that the transformation gives expected output over historical data
5. Verify with `get_online_features` on dev branch that the transformation correctly outputs online features
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