chore: Updating docs (#4346)

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

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",
 )

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"