From 660df6ec9b1191d1ea1b673c8a4fd5845c05f717 Mon Sep 17 00:00:00 2001 From: Francisco Arceo Date: Fri, 12 Jul 2024 01:26:03 -0400 Subject: [PATCH] chore: Updating docs (#4346) --- docs/README.md | 8 ++++---- ui/feature_repo/features.py | 22 +++++++++++----------- ui/package.json | 2 +- 3 files changed, 16 insertions(+), 16 deletions(-) diff --git a/docs/README.md b/docs/README.md index 66c7548440..eea372ded0 100644 --- a/docs/README.md +++ b/docs/README.md @@ -39,10 +39,10 @@ Feast is likely **not** the right tool if you ### Feast does not _fully_ solve * **reproducible model training / model backtesting / experiment management**: Feast captures feature and model metadata, but does not version-control datasets / labels or manage train / test splits. Other tools like [DVC](https://dvc.org/), [MLflow](https://www.mlflow.org/), and [Kubeflow](https://www.kubeflow.org/) are better suited for this. -* **batch + streaming feature engineering**: Feast primarily processes already transformed feature values (though it offers experimental light-weight transformations). Users usually integrate Feast with upstream systems (e.g. existing ETL/ELT pipelines). [Tecton](http://tecton.ai/) is a more fully featured feature platform which addresses these needs. -* **native streaming feature integration:** Feast enables users to push streaming features, but does not pull from streaming sources or manage streaming pipelines. [Tecton](http://tecton.ai/) is a more fully featured feature platform which orchestrates end to end streaming pipelines. -* **feature sharing**: Feast has experimental functionality to enable discovery and cataloguing of feature metadata with a [Feast web UI (alpha)](https://docs.feast.dev/reference/alpha-web-ui). Feast also has community contributed plugins with [DataHub](https://datahubproject.io/docs/generated/ingestion/sources/feast/) and [Amundsen](https://github.com/amundsen-io/amundsen/blob/4a9d60176767c4d68d1cad5b093320ea22e26a49/databuilder/databuilder/extractor/feast\_extractor.py). [Tecton](http://tecton.ai/) also more robustly addresses these needs. -* **lineage:** Feast helps tie feature values to model versions, but is not a complete solution for capturing end-to-end lineage from raw data sources to model versions. Feast also has community contributed plugins with [DataHub](https://datahubproject.io/docs/generated/ingestion/sources/feast/) and [Amundsen](https://github.com/amundsen-io/amundsen/blob/4a9d60176767c4d68d1cad5b093320ea22e26a49/databuilder/databuilder/extractor/feast\_extractor.py). [Tecton](http://tecton.ai/) captures more end-to-end lineage by also managing feature transformations. +* **batch + streaming feature engineering**: Feast primarily processes already transformed feature values but is investing in supporting batch and streaming transformations. +* **native streaming feature integration:** Feast enables users to push streaming features, but does not pull from streaming sources or manage streaming pipelines. +* **feature sharing**: Feast has experimental functionality to enable discovery and cataloguing of feature metadata with a [Feast web UI (alpha)](https://docs.feast.dev/reference/alpha-web-ui). Feast also has community contributed plugins with [DataHub](https://datahubproject.io/docs/generated/ingestion/sources/feast/) and [Amundsen](https://github.com/amundsen-io/amundsen/blob/4a9d60176767c4d68d1cad5b093320ea22e26a49/databuilder/databuilder/extractor/feast\_extractor.py). +* **lineage:** Feast helps tie feature values to model versions, but is not a complete solution for capturing end-to-end lineage from raw data sources to model versions. Feast also has community contributed plugins with [DataHub](https://datahubproject.io/docs/generated/ingestion/sources/feast/) and [Amundsen](https://github.com/amundsen-io/amundsen/blob/4a9d60176767c4d68d1cad5b093320ea22e26a49/databuilder/databuilder/extractor/feast\_extractor.py). * **data quality / drift detection**: Feast has experimental integrations with [Great Expectations](https://greatexpectations.io/), but is not purpose built to solve data drift / data quality issues. This requires more sophisticated monitoring across data pipelines, served feature values, labels, and model versions. ## Example use cases diff --git a/ui/feature_repo/features.py b/ui/feature_repo/features.py index e02bb3de5d..40a42a9e99 100644 --- a/ui/feature_repo/features.py +++ b/ui/feature_repo/features.py @@ -11,7 +11,7 @@ name="zipcode", description="A zipcode", tags={ - "owner": "danny@tecton.ai", + "owner": "danny@feast.ai", "team": "hack week", }, ) @@ -40,7 +40,7 @@ tags={ "date_added": "2022-02-7", "experiments": "experiment-A,experiment-B,experiment-C", - "access_group": "feast-team@tecton.ai", + "access_group": "feast-team@feast.ai", }, online=True, ) @@ -62,7 +62,7 @@ tags={ "date_added": "2022-02-7", "experiments": "experiment-A,experiment-B,experiment-C", - "access_group": "feast-team@tecton.ai", + "access_group": "feast-team@feast.ai", }, online=True, ) @@ -80,7 +80,7 @@ tags={ "date_added": "2022-02-7", "experiments": "experiment-A,experiment-B,experiment-C", - "access_group": "feast-team@tecton.ai", + "access_group": "feast-team@feast.ai", }, online=True, ) @@ -89,7 +89,7 @@ name="dob_ssn", description="Date of birth and last four digits of social security number", tags={ - "owner": "tony@tecton.ai", + "owner": "tony@feast.ai", "team": "hack week", }, ) @@ -121,7 +121,7 @@ tags={ "date_added": "2022-02-6", "experiments": "experiment-A", - "access_group": "feast-team@tecton.ai", + "access_group": "feast-team@feast.ai", }, online=True, ) @@ -157,7 +157,7 @@ def transaction_gt_last_credit_card_due(inputs: pd.DataFrame) -> pd.DataFrame: credit_history[["credit_card_due", "missed_payments_1y"]], zipcode_features, ], - tags={"owner": "tony@tecton.ai", "stage": "staging"}, + tags={"owner": "tony@feast.ai", "stage": "staging"}, description="Credit scoring model", ) @@ -167,7 +167,7 @@ def transaction_gt_last_credit_card_due(inputs: pd.DataFrame) -> pd.DataFrame: credit_history[["mortgage_due", "credit_card_due", "missed_payments_1y"]], zipcode_features, ], - tags={"owner": "tony@tecton.ai", "stage": "prod"}, + tags={"owner": "tony@feast.ai", "stage": "prod"}, description="Credit scoring model", ) @@ -178,7 +178,7 @@ def transaction_gt_last_credit_card_due(inputs: pd.DataFrame) -> pd.DataFrame: zipcode_features, transaction_gt_last_credit_card_due, ], - tags={"owner": "tony@tecton.ai", "stage": "dev"}, + tags={"owner": "tony@feast.ai", "stage": "dev"}, description="Credit scoring model", ) @@ -187,7 +187,7 @@ def transaction_gt_last_credit_card_due(inputs: pd.DataFrame) -> pd.DataFrame: features=[ zipcode_features, ], - tags={"owner": "amanda@tecton.ai", "stage": "dev"}, + tags={"owner": "amanda@feast.ai", "stage": "dev"}, description="Location model", ) @@ -196,6 +196,6 @@ def transaction_gt_last_credit_card_due(inputs: pd.DataFrame) -> pd.DataFrame: features=[ zipcode_money_features, ], - tags={"owner": "amanda@tecton.ai", "stage": "dev"}, + tags={"owner": "amanda@feast.ai", "stage": "dev"}, description="Location model", ) diff --git a/ui/package.json b/ui/package.json index de37f4394a..a380c65cfc 100644 --- a/ui/package.json +++ b/ui/package.json @@ -118,7 +118,7 @@ "Feature", "Store" ], - "author": "tony@tecton.ai", + "author": "tony@feast.ai", "license": "Apache-2.0", "bugs": { "url": "https://github.com/feast-dev/feast/issues"