Red Hat OpenShift AI

This tutorial shows you how to use the Llama Stack API to implement retrieval-augmented generation for an AI application built with Python.

Tackle the AI/ML lifecycle with OpenShift AI. This guide helps you build adaptable, production-ready MLOps workflows, from data preparation to live inference.

Learn how to use the CodeFlare SDK to submit RayJobs to a remote Ray cluster in OpenShift AI.

Learn how to deploy Open Platform for Enterprise AI ChatQnA application in OpenShift with AMD Instinct hardware.

Learn about the advantages of prompt chaining and the ReAct framework compared to simpler agent architectures for complex tasks.

Learn how to overcome compatibility challenges when deploying OpenShift AI and OpenShift Service Mesh 3 on one cluster.

Harness Llama Stack with Python for LLM development. Explore tool calling, agents, and Model Context Protocol (MCP) for versatile integrations.

Learn how to build a Model-as-a-Service platform with this simple demo. (Part 3 of 4)

Explore the architecture of a Models-as-a-Service (MaaS) platform and how enterprises can create a secure and scalable environment for AI models. (Part 2 of 4)

This article introduces Models-as-a-Service (MaaS) for enterprises, outlining the challenges, benefits, key technologies, and workflows. (Part 1 of 4)

Integrate Red Hat AI Inference Server with LangChain to build agentic document processing workflows. This article presents a use case and Python code.

Enhance your Node.js AI applications with distributed tracing. Discover how to use Jaeger and OpenTelemetry for insights into Llama Stack interactions.

Deploy AI at the edge with Red Hat OpenShift AI. Learn to set up OpenShift AI, configure storage, train models, and serve using KServe's RawDeployment.

In this recording, we demonstrate how to compose model compression experiments, highlighting the benefits of advanced algorithms requiring custom data sets and how evaluation results and model artifacts can be shared with stakeholders.

Podman enables developers to run Linux containers on MacOS within virtual machines, including GPU acceleration for improved AI inference performance.

Explore how to utilize guardrails for safety mechanisms in large language models (LLMs) with Node.js and Llama Stack, focusing on LlamaGuard and PromptGuard.

Members from the Red Hat Node.js team were recently at PowerUp 2025. It was held

Discover how IBM used OpenShift AI to maximize GPU efficiency on its internal AI supercomputer, using open source tools like Kueue for efficient AI workloads.

Gain detailed insights into vLLM deployments on OpenShift AI. Learn to build dashboards with Dynatrace and OpenTelemetry to enable reliable LLM performance.

Learn how to use Red Hat OpenShift AI to quickly develop, train, and deploy

Explore the complete machine learning operations (MLOps) pipeline utilizing Red

Optimize model inference and reduce costs with model compression techniques like quantization and pruning with LLM Compressor on Red Hat OpenShift AI.

Learn how to use synthetic data generation (SDG) and fine-tuning in Red Hat AI to customize reasoning models for your enterprise workflows.

Learn how to deploy a trained model with Red Hat OpenShift AI and use its

Explore how to use large language models (LLMs) with Node.js by observing Ollama