odh-23178-2 feature store SME review comments (#889)

* odh-23178-2 feature store SME review comments

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

Author: MelissaFlinn

assemblies/defining-ml-features.adoc

@@ -2,12 +2,16 @@
 
 ifdef::context[:parent-context: {context}]
 
+:context: featurestore-defining
+
 [id="defining-ml-features_{context}"]
-= Appendix A: Defining machine learning features
+= 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. 
 
+include::modules/setting-up-your-working-environment.adoc[leveloffset=+1]
+
 include::modules/about-feature-definitions.adoc[leveloffset=+1]
 
 include::modules/specifying-the-data-source-for-features.adoc[leveloffset=+1]
@@ -16,6 +20,7 @@ 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/overview-of-ml-features-and-feature-store.adoc

@@ -24,7 +24,7 @@ For more information about the support scope of {org-name} Technology Preview fe
 ====
 endif::[]
 
-A machine learning (ML) feature is a measurable property or attribute within a dataset that a machine learning model can analyze to learn patterns and make decisions. Examples of features include a customer's purchase history, demographic data like age and location, weather conditions, and financial market data. You can use these features to train models for tasks such as personalized product recommendations, fraud detection, and predictive maintenance.
+A machine learning (ML) feature is a measurable property or attribute within a data set that a machine learning model can analyze to learn patterns and make decisions. Examples of features include a customer's purchase history, demographic data like age and location, weather conditions, and financial market data. You can use these features to train models for tasks such as personalized product recommendations, fraud detection, and predictive maintenance.
 
 Feature Store is a {productname-long} component that provides a centralized repository that stores, manages, and serves machine learning features for both training and inference purposes.
 
@@ -41,12 +41,13 @@ include::modules/feature-store-workflow.adoc[leveloffset=+1]
 * For example Feature Store CRD configurations, see the
 link:https://github.com/feast-dev/feast/tree/master/infra/feast-operator/config/samples[Feast Operator configuration samples^].
 
-* For details about the Feast API, see the 
+* For details about the Feast CRD APIs, see the 
 link:https://github.com/feast-dev/feast/blob/stable/infra/feast-operator/docs/api/markdown/ref.md[Feast API documentation^].
 
 * For information on how to implement machine learning features, see the
 link:https://docs.feast.dev/getting-started/concepts[Feast documentation^].
 
+* For end-to-end use case examples of how Feature Store can benefit your AI/ML workflows, see https://docs.feast.dev/getting-started/use-cases[Feast Getting Started: Use Cases].
 
 ifdef::parent-context[:context: {parent-context}]
 ifndef::parent-context[:!context:]

assemblies/setting-up-feature-store.adoc

@@ -10,7 +10,7 @@ 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 them to use for model inference. 
+. Make features available to data scientists for model training and inference. 
 
 include::modules/before-you-begin.adoc[leveloffset=+1]
 
@@ -22,8 +22,6 @@ include::modules/adding-feature-definitions-and-initializing-your-feature-store-
 
 include::modules/specifying-files-to-ignore.adoc[leveloffset=+2]
 
-include::modules/making-features-available-for-real-time-inference.adoc[leveloffset=+1]
-
 include::modules/viewing-feature-store-objects-in-the-web-based-ui.adoc[leveloffset=+1]
 
 

modules/about-feature-definitions.adoc

@@ -3,9 +3,9 @@
 [id='about-feature-definitions_{context}']
 = About feature definitions
 
-A machine learning feature is a measurable property or attribute within a dataset that a machine learning model can analyze to learn patterns and make decisions. In Feature Store, you define a feature by defining the name and data type of an attribute. 
+A machine learning feature is a measurable property or _field_ within a data set that a machine learning model can analyze to learn patterns and make decisions. In Feature Store, you define a feature by defining the name and data type of a field. 
 
-A feature definition is a schema that includes the name and data type of an attribute, as shown in the following example:
+A feature definition is a schema that includes the field name and data type, as shown in the following example:
 
 [source,python]
 ----
@@ -20,7 +20,7 @@ credit_card_amount_due = Field(
 
 For a list of supported data types for fields in Feature Store, see the link:https://rtd.feast.dev/en/master/feast.html#module-feast.types[`feast.types` module] in the Feast documentation.
 
-In addition to field name and data type, a feature view can include additional metadata, specified as descriptions of features, as shown in the following example:
+In addition to field name and data type, a feature definition can include additional metadata, specified as descriptions of features, as shown in the following example:
 
 [source,python]
 ----

modules/about-organizing-features-by-using-entities.adoc

@@ -14,7 +14,7 @@ customer = Entity(name='dob_ssn', join_keys=['dob_ssn'])
 
 The entity name uniquely identifies the entity. The join key identifies the physical primary key on which feature values are joined together for feature retrieval.
 
-The following table shows example data with a single entity column (`dob_ssn`) and two feature columns (`credit_card_due` and `rating`).
+The following table shows example data with a single entity column (`dob_ssn`) and two feature columns (`credit_card_due` and `bankruptcies`).
 
 .Example data showing an entity and features
 [cols="1,3,2,3,2"]

modules/adding-feature-definitions-and-initializing-your-feature-store-instance.adoc

@@ -16,7 +16,8 @@ When you initialize the Feature Store instance, Feature Store completes the foll
 
 * Syncs the metadata about objects to the feature registry. If a registry does not exist, Feature Store creates one. The default registry is a simple Protobuf binary file on disk (locally or in an object store).
 
-* Creates or updates all necessary Feature Store infrastructure. The exact infrastructure that Feature Store creates depends on the provider configuration that you have set in `feature_store`.yaml. For example, setting `local` as your provider results in the creation of a sqlite online store.
+* Creates or updates all necessary Feature Store infrastructure. The exact infrastructure that Feature Store creates depends on the provider configuration that you have set in `feature_store.yaml`. 
+For example, when you specify `local` as your provider, Feature Store creates the infrastructure on the local cluster.
 +
 *Note:*  When you use a cloud provider, such as Google Cloud Platform or Amazon Web Service, the `feast apply` command creates cloud infrastructure that might incur costs for your organization.
 
@@ -37,8 +38,8 @@ When you initialize the Feature Store instance, Feature Store completes the foll
 . Click the `feast` pod and then select *Pod details*. 
 . Scroll down to see the online container. This container is the deployment for the online server, and it makes the feature server REST API available in the OpenShift cluster. 
 . Scroll up and then click *Terminal*. 
-. Copy the feature definition (`.py`) file to your Feature Store namespace.
-. To create a feature registry, set up data connections, and add the feature definitions to the registry, run the following command:
+. Copy the feature definition (`.py`) file to your Feature Store directory.
+. To create a feature registry and add the feature definitions to the registry, run the following command:
 +
 ----
 feast apply

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

@@ -6,7 +6,7 @@
 [role='_abstract']
 You can add an instance of Feature Store to a data science project by creating a custom resource definition (CRD) in the OpenShift console.
 
-The following example shows the minimum requirements for a Feature Store CRD YAML file:
+The following example shows the minimum requirements for a Feature Store CR YAML file:
 
 [.lines_space]
 [.console-input]
@@ -28,7 +28,7 @@ spec:
 
 * You have set up your database infrastructure for the online store, offline store, and registry. 
 +
-For an example of setting up and running PostgreSQL (for the registry) and Redis (for the online store), see the Featire Store Operator quick start example: https://github.com/feast-dev/feast/tree/stable/examples/operator-quickstart.
+For an example of setting up and running PostgreSQL (for the registry) and Redis (for the online store), see the Feature Store Operator quick start example: https://github.com/feast-dev/feast/tree/stable/examples/operator-quickstart.
 
 
 ifndef::upstream[]
@@ -70,7 +70,7 @@ The `spec.feastProjectDir` references a Feature Store project that is in the Git
 
 . Click *Create*.
 
-When you create the Feature Store CRD file in OpenShift, Feature Store starts a remote online feature server, and configures a default registry and an offline store with the local provider. 
+When you create the Feature Store CR in OpenShift, Feature Store starts a remote online feature server, and configures a default registry and an offline store with the local provider. 
 
 A _provider_ is a customizable interface that provides default Feature Store components, such as the registry, offline store, and online store, that target a specific environment, ensuring that these components can work together seamlessly. The local provider uses the following default settings:
 
@@ -158,6 +158,6 @@ The `feature_store.yaml` file defines the following components:
 
 .Next steps
 
-* Optionally, you can customize the default configurations for the offline store, online store, or registry by editing the YAML configuration for the Feature Store CRD, as described in _Customizing your feature store configuration_.
+* 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.

modules/feature-store-workflow.adoc

@@ -7,30 +7,29 @@ The Feature Store workflow involves the following tasks for ML engineers, OpenSh
 
 *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.
 
-
-*ML Engineer*
-
-* Creates a feature definition file.
-* Defines the data sources and other feature store objects
-
-For more information, see _Appendix A: Defining features_.
-
-
 *Cluster administrator*
 
 . 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, making features available to data scientists to use for model inference
+. 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*
 
 . Creates a workbench.
 . Obtains the `feature_store.yaml` from the cluster administrator.
-. Installs Feature Store in their IDE environment, for example Jupyter.
-. Uses `feast` commands to retrieve features for model training.
+. 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.
 
-For more information, see _Chapter 3. Retrieving features for model training_.
+For more information, see _Chapter 4. Retrieving features for model training_.

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

@@ -11,10 +11,11 @@ Materialization ensures that the same features are available for both model trai
 
 .Prerequisites
 
-* You have the `cluster-admin` role in {openshift-platform}.
+* In your IDE environment, you have installed the Feature Store Python SDK as described in _Setting up your working environment_. 
 
-* You have registered the feature views that you want to materialize, as described in _Adding feature definitions and initializing your Feature Store instance_.
+* Your cluster administrator has pushed feature data to the online store.
 
+* Optionally, you have cloned a Git repository that includes your model training code.
 
 .Procedure
 

modules/retrieving-online-features-for-model-inference.adoc

@@ -11,13 +11,13 @@ When the cluster administrator creates a `FeatureStore` CR, by default, it runs
 
 A feature service is an object that represents a logical group of features from one or more feature views. With a feature service, your ML models can access features from within a feature view as needed.
 
-Typically, you create one feature service per model version, allowing for tracking of the features used by models. The features retrieved from the online store can belong to multiple feature views.
+Typically, you create one feature service per model version, allowing for tracking of the features used by models. The features retrieved from the online store can belong to many feature views.
 
-*Note:* When you apply a feature service, an actual service is not deployed.
+*Note:* When you apply a feature service, an actual feature store server is not deployed.
 
 .Prerequisites
 
-* In your IDE environment, you have installed Feature Store and added the `feature_store.yaml` file provided by your cluster administrator, as described in _Setting up your working environment_. 
+* 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.