Odh 23178 2 Update Feature Store guide (#891)

* odh-23178-2 feature store SME review comments

* odh-23178-2 address PR and code rabbit comments

* odh-23178-2 feature store updates

* odh-23178-2 peer review comments

Author: MelissaFlinn

.DS_Store

@@ -2,13 +2,11 @@
 
 ifdef::context[:parent-context: {context}]
 
-:context: featurestore-defining
-
 [id="defining-ml-features_{context}"]
 = Defining machine learning features
 
 [role='_abstract']
-As part of the Feature Store workflow, ML engineers are responsible for identifying data sources and defining features of interest. 
+As part of the Feature Store workflow, ML engineers or data scientists are responsible for identifying data sources and defining features of interest. 
 
 include::modules/setting-up-your-working-environment.adoc[leveloffset=+1]
 
@@ -20,7 +18,5 @@ include::modules/about-organizing-features-by-using-entities.adoc[leveloffset=+1
 
 include::modules/creating-feature-views.adoc[leveloffset=+1]
 
-include::modules/making-features-available-for-real-time-inference.adoc[leveloffset=+1]
-
 ifdef::parent-context[:context: {parent-context}]
 ifndef::parent-context[:!context:]

assemblies/defining-ml-features.adoc

@@ -7,9 +7,9 @@ ifdef::context[:parent-context: {context}]
 
 [role='_abstract']
 
-After a cluster administrator configures Feature Store on {productname-long}, a data scientist can retrieve features to train models for inference.
+After a cluster administrator configures Feature Store on {productname-long}, an ML engineer or a data scientist can retrieve features to train models for inference.
 
-include::modules/setting-up-your-working-environment.adoc[leveloffset=+1]
+include::modules/making-features-available-for-real-time-inference.adoc[leveloffset=+1]
 
 include::modules/retrieving-online-features-for-model-inference.adoc[leveloffset=+1]
 

assemblies/retrieving-features-for-model-training.adoc

@@ -10,7 +10,8 @@ As a cluster administrator, you must complete the following tasks to set up Feat
 . Enable the Feature Store component.
 . Create a data science project and add a Feature Store instance.
 . Initialize the Feature Store instance.
-. Make features available to data scientists for model training and inference. 
+. Set up Feature Store so that ML Engineers and data scientists can push and retrieve features to use for model training and inference.
+
 
 include::modules/before-you-begin.adoc[leveloffset=+1]
 

assemblies/setting-up-feature-store.adoc

@@ -160,4 +160,4 @@ The `feature_store.yaml` file defines the following components:
 
 * Optionally, you can customize the default configurations for the offline store, online store, or registry by editing the YAML configuration for the Feature Store CR, as described in _Customizing your feature store configuration_.
 
-* Give your data scientists access to the data science project so that they can create a workbench. and provide them with a copy of the `feature_store.yaml` file so that they can add it to their workbench IDE, such as Jupyter.
+* Give your ML engineers and data scientists access to the data science project so that they can create a workbench. and provide them with a copy of the `feature_store.yaml` file so that they can add it to their workbench IDE, such as Jupyter.

modules/creating-a-feature-store-instance-in-a-data-science-project.adoc

@@ -4,7 +4,7 @@
 = Enabling the Feature Store component
 
 [role='_abstract']
-To allow the data scientists in your organization to work with machine learning features, you must enable the Feature Store component in {productname-long}.
+To allow the ML engineers and data scientists in your organization to work with machine learning features, you must enable the Feature Store component in {productname-long}.
 
 .Prerequisites
 

modules/enabling-the-feature-store-component.adoc

@@ -3,33 +3,27 @@
 [id='feature-store-workflow_{context}']
 =  Feature Store workflow
 
-The Feature Store workflow involves the following tasks for ML engineers, OpenShift cluster administrators, and data scientists:	
+The Feature Store workflow involves the following tasks OpenShift cluster administrators, and machine learning (ML) engineers or data scientists:	
 
-*Note:* This Feature Store workflow describes a local implementation. A production client-server architecture with full authorization and role assignments is planned for a future release.
+*Note:* This Feature Store workflow describes a local implementation that is available in this Technology Preview release.
 
 *Cluster administrator*
 
+Installs and configures Feature Store, as described in _Chapter 2. Configuring Feature Store_:
+
 . Installs OpenShift AI.
 . Enables the Feature Store component by using the Feature Store operator.
 . Creates a data science project.
 . In the data science project, creates a Feature Store instance by using a `feast.yaml` file that specifies the offline and online stores.
 . Sets up Feature Store so that ML Engineers and data scientists can push and retrieve features to use for model training and inference.
 
-For more information, see _Chapter 2. Configuring Feature Store_.
-
-*ML Engineer*
-
-* Creates a feature definition file.
-* Defines the data sources and other feature store objects.
-* Makes features available for real-time inference.
-
-For more information, see _Chapter 3: Defining features_.
-
-*Data scientist*
+*ML Engineer or data scientist*
 
-. Creates a workbench.
-. Obtains the `feature_store.yaml` from the cluster administrator.
-. Installs the Feature Store Python SDK in their IDE environment, for example Jupyter.
-. Uses `feast` Python APIs to retrieve features for model training in the workbench.
+* Prepares features, as described in _Chapter 3: Defining features_:
+.  Creates a feature definition file.
+.  Defines the data sources and other feature store objects.
+.  Makes features available for real-time inference.
 
-For more information, see _Chapter 4. Retrieving features for model training_.
+* Prepares features for model training and real-time inference, as described in _Chapter 4. Retrieving features for model training_:
+. Makes features available to models.
+. Uses `feast` Python APIs to retrieve features for model training and inference.

modules/feature-store-workflow.adoc

@@ -3,7 +3,7 @@
 [id="making-features-available-for-real-time-inference_{context}"]
 = Making features available for real-time inference
 
-To make features available for real-time inference for use by the data scientists on your team, load feature data to the online store.  
+To make features available for real-time inference for use by ML engineers and data scientists on your team, load or _materialize_ feature data to the online store.  
 
 Materialization ensures that the same features are available for both model training and real-time predictions, making your ML workflow robust and reproducible. The online store serves the latest features to models for online prediction. Data scientists on your team with access to your data science project can access the features in the online store.
 

modules/making-features-available-for-real-time-inference.adoc

@@ -32,7 +32,7 @@ ML platform teams use Feature Store to store and serve features consistently for
 
 Feature Store consists of the following key components:
 
-Registry:: A central catalog of all feature definitions and their related metadata. It allows data scientists to search, discover, and collaborate on new features. The registry exposes methods to apply, list, retrieve, and delete features.
+Registry:: A central catalog of all feature definitions and their related metadata. It allows ML engineers and data scientists to search, discover, and collaborate on new features. The registry exposes methods to apply, list, retrieve, and delete features.
 
 Offline Store:: The data store that contains historical data for scale-out batch scoring or model training. The offline store persists batch data that has been ingested into Feature Store. This data is used for producing training datasets. Examples of offline stores include Dask, Snowflake, BigQuery, Redshift, and DuckDB.
 

modules/overview-of-feature-store.adoc

@@ -19,10 +19,10 @@ Typically, you create one feature service per model version, allowing for tracki
 
 * In your IDE environment, you have installed the Feature Store Python SDK and added a `feature_store.yaml` file, as described in _Setting up your working environment_. 
 
-* Your cluster administrator has pushed feature data to the online store.
-
 * Optionally, you have cloned a Git repository that includes your model training code.
 
+* Feature data is loaded in the online store, as described in _Making features available for real-time inference_.
+
 .Procedure
 
 . From the {productname-short} dashboard, click *Data science projects*.