Artificial intelligence

Learn how to choose between vLLM and SGLang for production LLM inference by comparing PagedAttention, RadixAttention, and independent benchmark results.

Deploy AI-driven people matching on Red Hat OpenShift with PeopleMesh, using Docling and LLMs to automate profile creation from resumes.

Set up MCP servers for Red Hat Lightspeed, Satellite, and RHEL using goose, and learn key operational lessons on LLM token limits and API budgets.

Learn how to use the open source scanner garak to test AI models for

Learn how Red Hat built IdeaBot on OpenShift using isolated pods, auth proxies, and EARS-based BDD to secure and automate internal AI research.

Learn how Rossoctl manages agent security and observability on Red Hat OpenShift using A2A, SPIRE, and MLflow to prevent agent sprawl and enable zero trust AI.

Learn how to add NeMo Guardrails to LangGraph agents on Red Hat OpenShift AI using a proxy pattern to inspect requests without modifying agent code.

Compare Jev decision models with Red Hat guardrails to evaluate AI safety performance, cost, and latency for enterprise implementation.

Learn how to manage GPU quotas with Kueue and DRA on Red Hat OpenShift to enable fair sharing and accurate device-level accounting for AI workloads.

Learn how to test AI agent skills with skill-creator and promptfoo to prevent silent misfires and ensure reliable trigger behavior in CI pipelines.

Learn how to secure LLM supply chains by implementing behavioral probing and provenance verification to detect backdoors that evade standard model scanning.

Learn how to simplify distributed training with Kubeflow Trainer 2 on OpenShift AI by replacing framework-specific CRDs with a unified TrainJob resource.

Learn how to use garak, an open source LLM vulnerability scanner developed by NVIDIA, to perform red teaming on AI models.

Implement enterprise-wide automation with Pipeline Failure Analyzer: AI-powered CI/CD failure diagnosis.

Discover how to build cost-ranked fraud detection models in Python, combine predictive ML with LLM triage notes, and scale using Red Hat OpenShift AI.

Implement structured decision reads with DiffusionGemma on Red Hat AI to achieve low-latency, self-hosted AI for regulated industries.

Optimize LLM inference performance with a 71% latency reduction on NVIDIA H200 GPUs. Learn how to cut TTFT from 995ms to 287ms with smarter request routing.

Integrate Red Hat Satellite's MCP server with OpenShift Lightspeed for a single AI assistant with live infrastructure data and enterprise knowledge.

Add NeMo Guardrails to a LangGraph agent on Red Hat OpenShift AI without rewriting the graph. Jump to 5:28 to see the same in-cluster vLLM unguarded vs guarded: the unguarded agent answers a check-fraud prompt; NeMo Guardrails blocks it on content safety.
NeMo Guardrails is included in Red Hat OpenShift AI (upstream: NVIDIA NeMo Guardrails) and is deployed with a NeMo Guardrails custom resource managed by the TrustyAI Operator. This walkthrough covers the passthrough proxy, layered rails (regex → content safety → topic boundary → output safety), and opt-in tracing so you can see which rail fired.

Chapters
0:00 Introduction
0:17 What this agent does
1:02 Proxy architecture and rail order
2:25 Customizing rails and changing domain
4:23 Deploy locally and on OpenShift AI
4:58 Tracing overview
5:28 Unguarded vs guarded
8:04 Which rail fired
9:35 Wrap-up

Resources

Guardrailed Agent example:
https://github.com/red-hat-data-services/agentic-starter-kits/tree/main/agents/langgraph/examples/guardrailed_agent

Enable AI safety with NeMo Guardrails:
https://docs.redhat.com/en/documentation/red_hat_openshift_ai_self-managed/3.5/html/enabling_ai_safety_with_guardrails/enabling-ai-safety-with-nemo-guardrails_nemo-guardrails

Track AI inference costs by department using Red Hat OpenShift AI’s built-in MaaS gateway API keys, Perses dashboards, and MLflow tracing, with no manual instrumentation required.

Learn how to reduce Llama 3.1 8B Instruct model size and improve performance with W8A8 INT8 quantization.

Automate enterprise-wide RAG pipelines with Red Hat OpenShift AI's AutoRAG, speeding up AI optimization for your business.

Learn how to install and configure OpenCode, an open source AI coding assistant, for local development with Ollama, OpenVINO, or Red Hat AI.

Learn how to fine-tune your text-to-speech model for Turkish with Red Hat OpenShift AI and Kubeflow Trainer, reducing speech errors by over 90%.

Improve large language model inference speed with Speculators 0.6.0's FastMTP-style fine-tuning.