Artificial intelligence

Discover how Red Hat OpenShift AI 3.4's Models-as-a-Service (MaaS) capability streamlines AI inference by acting as an integrated AI gateway within the platform, providing centralized governance and routing requests to both self-hosted models and external providers.

Learn how to prevent silent failures in your production AI inference stack with end-to-end benchmarking.

Learn how to prevent GPU waste and financial loss by implementing just-in-time (JIT) checkpointing with Kubeflow Training SDK on OpenShift AI.

How to use a split disk configuration to solve disk space management issues, specifically in OpenShift clusters running large AI/ML workloads.

Learn what's new in Red Hat Enterprise Linux 10.2 and 9.8, including Red Hat build of Podman Desktop, advanced AI assistance, and database and toolset updates.

Learn about the five primary structural challenges in enterprise AI evaluation and how EvalHub addresses them with a unified foundation for AI evaluation.

Learn how our team implemented CI/CD pipelines for the it-self-service-agent AI quickstart and the benefits of using CI/CD for agentic systems.

Learn how Red Hat AI can help address the security challenges of AI agents in production, from semantic malware to container escapes.

Scale agentic AI with Red Hat’s trusted software factory. Use Policy as Code and SBOMs to strengthen your development pipeline and manage software provenance.

Deploy confidential containers and CVMs for AI. Learn how the Red Hat build of Trustee automates attestation to protect patient data on OpenShift and RHEL.

Learn how Red Hat AI 3.4 uses EvalHub to orchestrate AI evaluations on Kubernetes. Scale frameworks like Garak and LightEval with built-in MLflow tracking.

Learn how Kagenti ADK, an open source toolkit, handles the complexities of managing production AI agents. It aligns with the Linux Foundation's Agent2Agent (A2A) protocol and provides a set of runtime services for easier deployment and operation.

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

Learn how Red Hat made Storybook a verification engine for the access management interface on Red Hat Hybrid Cloud Console.

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.

This article describes an enterprise-preferred topology that separates fleet management from cluster hosting for independent scaling and clean operational boundaries.

This article discusses the benefits of diffusion LLMs, a revolutionary approach to language models that offers a dynamic tradeoff between accuracy and performance. The article covers the architecture, evolution, and real-world statistics of this technology, including examples of open source models like LLaDA 2.X and Mercury 2.

Discover how Helion, a Python embedded domain-specific language, abstracts low-level parallelism details to allow developers to write GPU operations using simple, intuitive PyTorch-like syntax. Automatically generate hundreds or even thousands of Triton variants for optimal performance.

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

This article describes how to onboard a project and the results using two tools, CodeCov and CodeRabbit.

Discover OpenCode, a model-neutral AI coding assistant that supports over 75 providers, including OpenAI, Anthropic Claude, Google Gemini, and local large language models (LLMs) via Ollama. Switch models on demand, compare outputs, avoid vendor lock-in, and even run fully offline with local models. Learn how to set up your environment in Red Hat OpenShift Dev Spaces.

Learn to modernize enterprise Java for Kubernetes using the four-path framework. Explore cloud-native patterns like Sidecars, Sagas, and RAG to build resilient, AI-ready systems.

Learn how to combine KServe and llm-d to optimize generative AI inference, improve performance, and reduce infrastructure costs. This article demonstrates the integration architecture and provides practical guidance for AI platform teams.

Learn how to automate documentation updates for code changes using Code-to-Docs, an open source GitHub Action. This tool uses AI to analyze your code changes, identify affected documentation files, and generate updated content. Get started with this guide.

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.