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* fix: boilerplate reduction 33 * fix: project ID * fix: df type * fix: uuid * fix: uuid * fix: uuid * fix: uuid * fix: timestamp * fixes the timestamp issue + cleans up the descriptions * ran linter test --------- Co-authored-by: Andrew Ferlitsch <aferlitsch@google.com>
Learn how to ingest features from a `Pandas DataFrame` into your Vertex AI Feature Store using `write_feature_values` method from the Vertex AI SDK.
The steps performed include:
- Create `Feature Store`
- Create new `Entity Type` for your `Feature Store`
- Ingest feature values from `Pandas DataFrame` into `Feature Store`'s `Entity Types`.
Learn more about Vertex AI Feature Store.
Using Vertex AI Feature Store with Pandas Dataframe
Learn how to use `Vertex AI Feature Store` with pandas Dataframe.
The steps performed include:
- Ingest Feature values from Pandas DataFrame into Feature Store's Entity types.
- Read Entity feature values from Online Feature Store into Pandas DataFrame.
- Batch serve feature values from your Feature Store into Pandas DataFrame.
- Online serving with updated feature values.
- Point-in-time correctness to fetch feature values for training.
Learn more about Vertex AI Feature Store.
Online and Batch predictions using Vertex AI Feature Store
Learn how to use `Vertex AI Feature Store` to import feature data, and to access the feature data for both online serving and offline tasks, such as training.
The steps performed include:
- Create featurestore, entity type, and feature resources.
- Import feature data into `Vertex AI Feature Store` resource.
- Serve online prediction requests using the imported features.
- Access imported features in offline jobs, such as training jobs.
- Use streaming ingestion to ingest small amount of data.
Learn more about Vertex AI Feature Store.