docs: Adding example of RHOAI feast quick start guide (#4564)

* Adding example of RHOAI feast quick start guide

Signed-off-by: Lokesh Rangineni <[email protected]>

* Incorporating code review comments.
Adding the missed link in the index page of root examples directory.

Signed-off-by: Lokesh Rangineni <[email protected]>

* Incorporating code review comments from the @daniel Dowler
Added more details about RHOAI platform and reference to the documentation.
Incorporated other minor comments.

Signed-off-by: Lokesh Rangineni <[email protected]>

* Incorporating code review comment from Francisco.

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Signed-off-by: Lokesh Rangineni <[email protected]>

examples/README.md

@@ -14,3 +14,5 @@
 
 7. **[Podman/Podman Compose_local](https://github.com/feast-dev/feast/tree/master/examples/podman_local)**: Demonstrates how to deploy Feast remote server components using Podman Compose locally.
 
+8. **[RHOAI Feast Demo](https://github.com/feast-dev/feast/tree/master/examples/rhoai-quickstart)**: Showcases Feast's core functionality using a Jupyter notebook, including fetching online feature data from a remote server and retrieving metadata from a remote registry.
+

examples/rhoai-quickstart/README.md

@@ -0,0 +1,45 @@
+# Quickstart: Running Feast example
+
+This quickstart guide will walk you through setting up and using [Feast](https://feast.dev) as a feature store on Red Hat OpenShift AI. By the end of this tutorial, you’ll have the environment configured, sample data loaded, and features retrieved using Feast objects.
+
+## Prerequisites
+This example uses Jupyter Notebook to showcase Feast's capabilities. You'll need an environment where Jupyter Notebook can be executed.
+
+You have two options for setting up the runtime environment:
+1. **Running Jupyter on your local machine**: If you're new to Jupyter, refer to the official documentation [here](https://docs.jupyter.org/en/latest/running.html).
+2. **Running Jupyter on Red Hat OpenShift AI (RHOAI)**: If you'd prefer to run Jupyter on the RHOAI platform, follow the instructions below.
+
+## Using the Red Hat OpenShift AI (RHOAI) Platform
+You can execute the Jupyter notebook directly on the RHOAI platform. If you don't have an existing RHOAI cluster, you can try this Feast example in the developer sandbox.
+
+To get started, visit the [Red Hat OpenShift AI sandbox](https://www.redhat.com/en/technologies/cloud-computing/openshift/openshift-ai) and launch your environment.
+
+### Getting Started with RHOAI
+Before proceeding, it's helpful to familiarize yourself with Red Hat OpenShift AI. If you're new to the platform, check out this [short introductory video](https://youtu.be/75WtOSpn5qU?si=uT1xZfpuJBkVP7ha) for a quick overview of its features.
+
+For detailed documentation on RHOAI, including how to work on data science projects, refer to the official product documentation [here](https://docs.redhat.com/en/documentation/red_hat_openshift_ai_cloud_service/1/html/working_on_data_science_projects/using-data-science-projects_projects).
+
+### Steps to Set Up a Workbench on the RHOAI Sandbox
+Follow these brief steps to create a workbench on the RHOAI sandbox:
+
+1. Navigate to your project (namespace) → **Dev**.
+2. Go to the **Workbenches** tab.
+3. Select **Create Workbench** and provide the necessary details.
+4. Click **Create**.
+
+Once your workbench is set up, you can import and run the Feast example using one of these methods:
+1. **Clone the GitHub repository**: Clone the repo into your RHOAI workbench and run the Jupyter notebook.
+2. **Upload files**: Upload the necessary files to your existing workbench and execute the notebook.
+
+## Notebook Overview
+The [feast-demo-quickstart.ipynb](feast-demo-quickstart.ipynb) notebook will guide you through:
+
+This notebook will use Driver entity (or model) to demonstrate the feast functionalities. 
+You should be able to execute the same jupyter notebook in a standalone environment as well.
+
+- **Setting up the Feast repository**: Load sample driver data, generate training datasets, run offline inference, and ingest batch features into the online store. You'll also learn how to fetch features for inference using Feast’s `FeatureView` and `FeatureService`.
+
+- **Configuring a Remote Online Topology**: Set up a remote online server and client, and retrieve features in real-time using the remote online client.
+
+- **Configuring a Remote Registry Topology**: Set up a remote registry server and client, and retrieve Feast metadata using the remote registry client.
+