OSDOCS-13287: adds RHOAI to MicroShift

Author: ShaunaDiaz

_attributes/attributes-microshift.adoc

@@ -6,6 +6,8 @@
 :OCP: OpenShift Container Platform
 :ocp-version: 4.18
 :oc-first: pass:quotes[OpenShift CLI (`oc`)]
+:rhoai-full: Red{nbsp}Hat OpenShift AI
+:rhoai: RHOAI
 //OpenShift Kubernetes Engine
 :oke: OpenShift Kubernetes Engine
 :product-title-first: Red Hat build of MicroShift (MicroShift)

_topic_maps/_topic_map_ms.yml

@@ -99,19 +99,6 @@ Topics:
 - Name: Listing update package contents
   File: microshift-list-update-contents
 ---
-Name: Support
-Dir: microshift_support
-Distros: microshift
-Topics:
-- Name: The etcd service
-  File: microshift-etcd
-- Name: The sos report tool
-  File: microshift-sos-report
-- Name: Getting your cluster ID
-  File: microshift-getting-cluster-id
-- Name: Getting support
-  File: microshift-getting-support
----
 Name: Configuring
 Dir: microshift_configuring
 Distros: microshift
@@ -198,6 +185,13 @@ Topics:
 - Name: Understanding storage migration
   File: microshift-storage-migration
 ---
+Name: Using AI models
+Dir: microshift_ai
+Distros: microshift
+Topics:
+- Name: Using artificial intelligence with MicroShift
+  File: microshift-rhoai
+---
 Name: Running applications
 Dir: microshift_running_apps
 Distros: microshift
@@ -239,6 +233,19 @@ Topics:
 - Name: Automated recovery from manual backups
   File: microshift-auto-recover-manual-backup
 ---
+Name: Support
+Dir: microshift_support
+Distros: microshift
+Topics:
+- Name: The etcd service
+  File: microshift-etcd
+- Name: The sos report tool
+  File: microshift-sos-report
+- Name: Getting your cluster ID
+  File: microshift-getting-cluster-id
+- Name: Getting support
+  File: microshift-getting-support
+---
 Name: Troubleshooting
 Dir: microshift_troubleshooting
 Distros: microshift

microshift_ai/_attributes

@@ -0,0 +1 @@
+../_attributes/

microshift_ai/images

@@ -0,0 +1 @@
+../images/

microshift_ai/microshift-rhoai.adoc

@@ -0,0 +1,14 @@
+:_mod-docs-content-type: ASSEMBLY
+[id="microshift-rh-openshift-ai"]
+include::_attributes/attributes-microshift.adoc[]
+= Using Red Hat OpenShift AI with {microshift-short}
+:context: microshift-rh-openshift-ai
+
+toc::[]
+
+Learn how to serve artificial intelligence (AI) models on your {microshift-short} edge deployments in a lightweight manner.
+
+include::modules/microshift-rhoai-con.adoc[leveloffset=+1]
+
+//include::modules/    [leveloffset=+1]
+

microshift_ai/modules

@@ -0,0 +1 @@
+../modules/

microshift_ai/snippets

@@ -0,0 +1 @@
+../snippets/

modules/microshift-rhoai-con.adoc

@@ -0,0 +1,17 @@
+// Module included in the following assemblies:
+//
+// * microshift_ai/microshift-rhoai.adoc
+
+:_mod-docs-content-type: CONCEPT
+[id="microshift-rhoai-con_{context}"]
+= How {rhoai-full} works in {microshift-short}
+
+You can train an artificial intelligence (AI) model in the cloud or datacenter on {OCP}, then serve your model in your edge deployments on {microshift-short}.
+
+{rhoai-full} ({rhoia}) is a platform for data scientists and developers of AI and machine learning (AI/ML) applications.
+
+{rhoia} provides an environment to develop, train, serve, test, and monitor AI/ML models and applications on-premise or in the cloud.
+
+For data scientists, OpenShift AI includes Jupyter and a collection of default notebook images optimized with the tools and libraries required for model development, and the TensorFlow and PyTorch frameworks. Deploy and host your models, integrate models into external applications, and export models to host them in any hybrid cloud environment. You can enhance your data science projects on OpenShift AI by building portable machine learning (ML) workflows with data science pipelines, using Docker containers. You can also accelerate your data science experiments through the use of graphics processing units (GPUs) and Intel Gaudi AI accelerators.
+
+For administrators, OpenShift AI enables data science workloads in an existing Red Hat OpenShift or ROSA environment. Manage users with your existing OpenShift identity provider, and manage the resources available to notebook servers to ensure data scientists have what they require to create, train, and host models. Use accelerators to reduce costs and allow your data scientists to enhance the performance of their end-to-end data science workflows using graphics processing units (GPUs) and Intel Gaudi AI accelerators.