Examples of validated patterns

This learning path deploys the Multicloud GitOps validated pattern, but there are a variety of use cases available to suit your needs. All of the validated patterns follow the same basic deployment process found in the previous resource.

• Information about the RAG LLM, Zero Trust, Hypershift, and OpenShift AI validated patterns

• None

There are a variety of validated patterns available to deploy and use for your own use cases. 

AI generation with RAG and LLM

This pattern showcases a chatbot LLM application augmented with data from Red Hat product documentation running on Red Hat OpenShift. It deploys an LLM application that connects to multiple LLM providers such as OpenAI, Hugging Face, and NVIDIA NIM. The application generates a project proposal for a Red Hat product. It can be deployed with multiple variants and partners, as seen in the interactive experience below, where the pattern is deployed on Microsoft Azure Red Hat OpenShift. 

This is a tested tier pattern. Learn more about it here.

Layered zero trust

Zero trust is an approach to security architecture based on the idea that every interaction starts in an untrusted state. The Layered Zero Trust pattern shows how to implement zero trust in a Red Hat OpenShift Container Platform environment. The pattern identifies specific transactions between an actor and a resource. For these transactions, you can determine the context and enforce policies.

The pattern addresses the shortcomings of traditional cybersecurity methods, such as defensive hardening and reactive detection. It is particularly effective for the following types of systems and environments:

• Distributed systems, such as cloud and edge environments.
• Autonomous and artificial intelligence (AI) or machine learning (ML) based systems, including robotic process automation.
• Large, composite systems that integrate third-party or legacy components.

This is a sandbox tier pattern. Learn about it here.

HyperShift (hosted control planes)

This pattern simplifies the deployment of a hosted control plane (HCP) or hosted control plane cluster. You can use this pattern to create HCP clusters.

This is a sandbox tier pattern. Learn more about it here.

Red Hat OpenShift AI

OpenShift AI is a platform for managing the lifecycle of predictive and generative AI (gen AI) models, at scale, across hybrid cloud environments. This pattern is deployed using OpenShift GitOps and is comprised of OpenShift AI and OpenShift Pipelines. This pattern deploys the minimum capabilities necessary for consumers to quickly start implementing their MLOps or AI workloads in an automated way. It is effective for the following use cases:

• Use a GitOps approach to manage hybrid and multi-cloud deployments across both public and private clouds
• Enable cross-cluster governance and application lifecycle management
• Securely manage secrets across the deployment

This is a sandbox tier pattern. Learn more about it here.

• Troubleshoot with Red Hat support*
• OpenShift Commons Validated Patterns Slack channel
• GitHub repositories for Validated Patterns

*Red Hat supports subscribed Red Hat products used in validated patterns. The codified patterns are distributed as community projects and no additional support beyond subscribed Red Hat products is implied.