GitBook: [master] 3 pages modified

docs/getting-started/concepts/README.md

@@ -12,5 +12,7 @@
 
 {% page-ref page="feature-retrieval.md" %}
 
+{% page-ref page="point-in-time-joins.md" %}
+
 
 

docs/getting-started/concepts/feature-retrieval.md

@@ -26,6 +26,10 @@ online_features = fs.get_online_features(
 
 It is possible to retrieve features from multiple feature views with a single request, and Feast is able to join features from multiple tables in order to build a training dataset. However, It is not possible to reference \(or retrieve\) features from multiple projects at the same time.
 
+{% hint style="info" %}
+Note, if you're using [Feature views without entities](feature-view.md#feature-views-without-entities), then those features can be added here without additional entity values in the `entity_rows`
+{% endhint %}
+
 ## Event timestamp
 
 The timestamp on which an event occurred, as found in a feature view's data source. The entity timestamp describes the event time at which a feature was observed or generated.

docs/getting-started/concepts/feature-view.md

@@ -70,3 +70,38 @@ Together with [data sources](data-source.md), they indicate to Feast where to fi
 
 Feature names must be unique within a [feature view](feature-view.md#feature-view).
 
+## \[Alpha\] On demand feature views
+
+On demand feature views allows users 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:
+
+```python
+# Define a request data source which encodes features / information only 
+# available at request time (e.g. part of the user initiated HTTP request)
+input_request = RequestDataSource(
+    name="vals_to_add",
+    schema={
+        "val_to_add": ValueType.INT64,
+        "val_to_add_2": ValueType.INT64
+    }
+)
+
+# Use the input data and feature view features to create new features
+@on_demand_feature_view(
+   inputs={
+       'driver_hourly_stats': driver_hourly_stats_view,
+       'vals_to_add': input_request
+   },
+   features=[
+     Feature(name='conv_rate_plus_val1', dtype=ValueType.DOUBLE),
+     Feature(name='conv_rate_plus_val2', dtype=ValueType.DOUBLE)
+   ]
+)
+def transformed_conv_rate(features_df: pd.DataFrame) -> pd.DataFrame:
+    df = pd.DataFrame()
+    df['conv_rate_plus_val1'] = (features_df['conv_rate'] + features_df['val_to_add'])
+    df['conv_rate_plus_val2'] = (features_df['conv_rate'] + features_df['val_to_add_2'])
+    return df
+```
+
+
+