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Update docs to include Snowflake/DQM and removing unused docs from old versions of Feast (feast-dev#2268)
* Update docs to include Snowflake, update roadmap to include DQM, fix quickstart outdated issues, and remove unnecessary docs from old Feast versions Signed-off-by: Danny Chiao <[email protected]> * fix template Signed-off-by: Danny Chiao <[email protected]> * fix template Signed-off-by: Danny Chiao <[email protected]> * Reference dqm guide Signed-off-by: Danny Chiao <[email protected]>
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‎README.md‎

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Please see our [documentation](https://docs.feast.dev/) for more information about the project.
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## 📐 Architecture
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<img src="https://i.imgur.com/DphraPo.png" width="750">
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![](docs/assets/feast-marchitecture.png)
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The above architecture is the minimal Feast deployment. Want to run the full Feast on GCP/AWS? Click [here](https://docs.feast.dev/how-to-guides/feast-gcp-aws).
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* [ ] Delete API
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* [ ] Feature Logging (for training)
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* **Data Quality Management (See [RFC](https://docs.google.com/document/d/110F72d4NTv80p35wDSONxhhPBqWRwbZXG4f9mNEMd98/edit))**
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* [ ] Data profiling and validation (Great Expectations) (Planned for Q1 2022)
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* [x] Data profiling and validation (Great Expectations)
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* [ ] Metric production
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* [ ] Training-serving skew detection
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* [ ] Drift detection
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* **Feature Discovery and Governance**
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* [x] Python SDK for browsing feature registry
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* [x] CLI for browsing feature registry
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* [x] Model-centric feature tracking (feature services)
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* [x] Amundsen integration (see [Feast extractor](https://github.com/amundsen-io/amundsen/blob/main/databuilder/databuilder/extractor/feast_extractor.py))
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* [ ] REST API for browsing feature registry
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* [ ] Feast Web UI
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* [ ] Feature versioning
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* [ ] Amundsen integration
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## 🎓 Important Resources
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<a href="https://github.com/feast-dev/feast/graphs/contributors">
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<img src="https://contrib.rocks/image?repo=feast-dev/feast" />
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</a>
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</a>

‎docs/README.md‎

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Feast (**Fea**ture **St**ore) is an operational data system for managing and serving machine learning features to models in production. Feast is able to serve feature data to models from a low-latency online store (for real-time prediction) or from an offline store (for scale-out batch scoring or model training).
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![](.gitbook/assets/feast-marchitecture-211014.png)
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![](assets/feast-marchitecture.png)
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## Problems Feast Solves
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**Feature discovery:** We also aim for Feast to include a first-class user interface for exploring and discovering entities and features.
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**‌Feature validation:** We additionally aim for Feast to improve support for statistics generation of feature data and subsequent validation of these statistics. Current support is limited.
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**Feature validation:** We additionally aim for Feast to improve support for statistics generation of feature data and subsequent validation of these statistics. Current support is limited.
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## What Feast is not
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‎docs/SUMMARY.md‎

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* [Architecture](getting-started/architecture-and-components/README.md)
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* [Overview](getting-started/architecture-and-components/overview.md)
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* [Feature repository](getting-started/architecture-and-components/feature-repository.md)
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* [Registry](getting-started/architecture-and-components/untitled.md)
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* [Registry](getting-started/architecture-and-components/registry.md)
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* [Offline store](getting-started/architecture-and-components/offline-store.md)
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* [Online store](getting-started/architecture-and-components/online-store.md)
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* [Provider](getting-started/architecture-and-components/provider.md)
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* [.feastignore](reference/feature-repository/feast-ignore.md)
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* [Feature servers](reference/feature-servers/README.md)
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* [Local feature server](reference/feature-servers/local-feature-server.md)
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* [\[Alpha\] Data quality monitoring](reference/dqm.md)
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* [\[Alpha\] On demand feature view](reference/alpha-on-demand-feature-view.md)
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* [\[Alpha\] Stream ingestion](reference/alpha-stream-ingestion.md)
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* [\[Alpha\] AWS Lambda feature server](reference/alpha-aws-lambda-feature-server.md)

‎docs/advanced/audit-logging.md‎

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‎docs/advanced/metrics.md‎

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