Red Hat OpenShift AI

Learn about our team's experience implementing a defense-in-depth safety architecture for AI agents using Llama Stack shields.

Learn how the Red Hat OpenShift AI observability summarizer transforms raw time-series data from Prometheus into actionable, human-readable insights for platform teams. Discover the five-layer pipeline architecture and how it reduces noise and increases signal for a focused answer.

Learn how to use OpenViking context database instead of traditional flat vector storage to provide AI agents with persistent, structured memory.

Learn about critical lessons from building an MCP-powered AI agent for ServiceNow, including how to structure testing environments, best practices for implementing safeguards, and a phased approach to deploying enterprise AI integrations.

Explore how Red Hat AI simplifies agent deployment with OpenClaw, showcasing model inference, safety guardrails, agent identity, and persistent state. Learn about vLLM, Llama Stack, and Models-as-a-Service (MaaS) options, and discover the benefits of agent identity and zero trust with Kagenti and AuthBridge.

Explore how Red Hat OpenShift AI was used to run an unsupervised AI agent for 24 hours, improving validation loss by 2.3% without human intervention. Learn about the container build, Kubernetes deployment, and key findings.

Learn how to set up distributed tracing for an agentic workflow based on lessons learned while developing the it-self-service-agent AI quickstart. This post covers configuring OpenTelemetry to track requests end-to-end across application workloads, MCP servers, and Llama Stack.

Explore the spending transaction monitor AI quickstart, demonstrating agentic AI for intelligent financial monitoring on enterprise-grade infrastructure. Lower the barrier to entry for AI experimentation and refine your AI strategy.

Explore the four pillars of AI coding: vibes, secs, skills, and agents, and learn how they can improve the coding quality and reduce the encoding/decoding gap. Discover the benefits of a spec-driven approach and the importance of modular specs and skills in achieving harmony.

Learn how to integrate Anthropic's Claude Code, an agentic coding tool, using Red Hat AI Inference Server on OpenShift. Keep the inference process private on your own infrastructure while retaining the full Claude Code workflow.

Follow this 4-step process using Training Hub and OpenShift AI to transition LLM fine-tuning from local experiments to repeatable, production-grade workflows.

Learn how to deploy multiple large language models (LLMs) behind a single OpenAI-compatible endpoint on OpenShift using a Model-as-a-Service (MaaS) approach. This guide demonstrates how to build an intelligent routing infrastructure that dynamically inspects the request payload and directs traffic based on the specified model field, reducing GPU waste and simplifying application logic.

Learn how to build reliable AI agents with our 8-stage evaluation framework. We explore DeepEval, multi-turn testing, and CI/CD integration for Red Hat AI.

Discover a practical solution pattern for building a modern financial application that makes loan decisions using multiple machine learning systems deployed across hybrid environments.

Learn how to enable the NVIDIA RTX PRO 4500 Blackwell Server Edition on Red Hat AI for compact, power-efficient AI deployments. This hardware offers inference performance without adding unnecessary operational complexity for Red Hat AI users.

Learn how to streamline Red Hat OpenShift AI dependency management using Kustomize and GitOps. Use the odh-gitops repo to automate setup via CLI or Argo CD.

This video demonstrates how to deploy Red Hat OpenShift AI dependencies using the odh-gitops repository with Kustomize and the command line.

This video demonstrates how to deploy Red Hat OpenShift AI dependencies using Argo CD and the odh-gitops repository.

Discover how to optimize training of MoE models with fms-hf-tuning, an open source tuning library for PyTorch FSDP and Hugging Face libraries. Learn about preprocessing data, throughput and memory efficiency features, distributed training, and expert parallelism. Improve your AI and agentic applications on domain-specific enterprise tasks.

Learn how to manage the security threats and access controls associated with adopting the new Agent Skills functionality.

Learn how the Responses API in Llama Stack automates complex tool calling while maintaining granular control over conversation flow for AI agents. Discover the benefits and implementation details.

Learn how to estimate memory requirements for your LLM fine-tuning experiments using Red Hat Training Hub's memory_estimator.py API. This guide covers the memory components, adjusting training setups for specific GPU specifications, and using the memory estimator in your code. Streamline your model fine-tuning process with runtime estimates and automated hyperparameter suggestions.

Learn how to deploy and test an Earth and space model inference service on Red Hat AI Inference Server and Red Hat OpenShift AI. This article includes two self-contained activities, one deploying Prithvi using a traditional Deployment object and another serving the model using KServe and observing Knative scaling.

Optimize vLLM performance with practical tuning tips. Learn how to use GuideLLM for benchmarking, adjust GPU ratios, and maximize KV cache to improve throughput.

Learn how to fine-tune AI pipelines in Red Hat OpenShift AI 3.3. Use Kubeflow Trainer and modular components for reproducible, production-grade model tuning.