chore: Updating broken links for on demand transformation (feast-dev#…

…4759)

docs/getting-started/concepts/dataset.md

@@ -7,7 +7,7 @@ Dataset's metadata is stored in the Feast registry and raw data (features, entit
 Dataset can be created from:
 
 1. Results of historical retrieval
-2. \[planned] Logging request (including input for [on demand transformation](../../reference/alpha-on-demand-feature-view.md)) and response during feature serving
+2. \[planned] Logging request (including input for [on demand transformation](../../reference/beta-on-demand-feature-view.md)) and response during feature serving
 3. \[planned] Logging features during writing to online store (from batch source or stream)
 
 ### Creating a saved dataset from historical retrieval

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

@@ -6,7 +6,14 @@
 **Note**: Feature views do not work with non-timestamped data. A workaround is to insert dummy timestamps.
 {% endhint %}
 
-A feature view is an object that represents a logical group of time-series feature data as it is found in a [data source](data-ingestion.md). Depending on the kind of feature view, it may contain some lightweight (experimental) feature transformations (see [\[Alpha\] On demand feature views](feature-view.md#alpha-on-demand-feature-views)).
+A **feature view** is defined as a *collection of features*. 
+
+- In the online settings, this is a *stateful* collection of 
+features that are read when the `get_online_features` method is called. 
+- In the offline setting, this is a *stateless* collection of features that are created when the `get_historical_features` 
+method is called. 
+
+A feature view is an object representing a logical group of time-series feature data as it is found in a [data source](data-ingestion.md). Depending on the kind of feature view, it may contain some lightweight (experimental) feature transformations (see [\[Beta\] On demand feature views](../../reference/beta-on-demand-feature-view.md)).
 
 Feature views consist of:
 

docs/getting-started/faq.md

@@ -55,7 +55,7 @@ Yes. In earlier versions of Feast, we used Feast Spark to manage ingestion from
 
 There are several kinds of transformations:
 
-* On demand transformations (See [docs](../reference/alpha-on-demand-feature-view.md))
+* On demand transformations (See [docs](../reference/beta-on-demand-feature-view.md))
   * These transformations are Pandas transformations run on batch data when you call `get_historical_features` and at online serving time when you call \`get\_online\_features.
   * Note that if you use push sources to ingest streaming features, these transformations will execute on the fly as well
 * Batch transformations (WIP, see [RFC](https://docs.google.com/document/d/1964OkzuBljifDvkV-0fakp2uaijnVzdwWNGdz7Vz50A/edit))

docs/tutorials/validating-historical-features.md

@@ -173,7 +173,7 @@ def on_demand_stats(inp: pd.DataFrame) -> pd.DataFrame:
     return out
 ```
 
-*Read more about on demand feature views [here](https://docs.feast.dev/reference/alpha-on-demand-feature-view)*
+*Read more about on demand feature views [here](../reference/beta-on-demand-feature-view.md)*
 
 
 ```python