Data science pipelines and model serving using Red Hat OpenShift Data Science
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Updated
Mar 27, 2025 - Python
Data science pipelines and model serving using Red Hat OpenShift Data Science
Production-pattern Red Hat OpenShift AI 3.4.0 platform with bare-metal ESXi, GPU passthrough, KServe RawDeployment, DeepSeek R1 inference at 12–17 tok/s
Deploy Cisco AI Pods with Ansible and Python
This guide is designed to help you deploy OpenShift on bare metal for a variety of workloads, providing flexibility, scalability, and advanced configurations tailored to diverse use cases.
Sanitized reference architecture for building a private family-focused AI platform on Red Hat OpenShift, OpenShift AI, OpenShift Virtualization, AAP, Strix Halo, and Apple Silicon.
Validated Pattern that deploys OpenShift AI and OpenShift Pipelines.
OpenShift 4.20 installation (Day 0/1/2) & architecture matrix across bare metal, hypervisors, cloud & air-gap. AI-generated with Gemini 3.8 Flash; not yet tested on live clusters, serves as an architecture reference.
Red Hat OpenShift AI Model Registry Deployment and Automated Model Ingestion
This repository contains the code for a workshop on how to use different tools to monitor and manage an edge-anomaly-detection application.
4 production-ready Agent Skills for OpenShift AI 3.4 — platform architecture, MaaS governance, AI gateway routing, and inference optimization
Personal blog exploring AI infrastructure, platform engineering, and cloud architecture. Topics include NVIDIA AI Infrastructure & Operations, InfiniBand networking, Kubernetes/OpenShift, agentic AI with LangGraph, and enterprise MLOps.
Production-ready AgentOps showcase on OpenShift AI: secure agent isolation (OpenShell), guardrails (NeMo), observability (MLflow), and a live BYOA demo with OpenClaw.
OpenShift Validated Reference Design for Enterprise AI
AWS 에 OpenShift 를 IPI 로 설치하고 agent OSS 스택과 Red Hat OpenShift AI 를 올려 GPU 서빙·LoRA 튜닝까지 해 본 뒤 깨끗하게 지우는 실습 레포
Edge AI inference on OpenShift AI — GGUF LLM serving via llama.cpp and Lemonade, YOLO26 object detection and segmentation, and a unified multi-model web UI, running on a three-node SNUC cluster with AMD Ryzen AI iGPUs.
My Personal Blog, learn, share and discover new things
Making Inference work for you with vLLM and RHOAI Enterprise Model Serving
Charter, guidelines, and plugin catalog for the Red Hat AI Community Plugins initiative
Benchmark study: NVIDIA L4 + Llama 3.1 8B FP8 on Red Hat OpenShift AI. Concurrent load benchmarks, quality evaluation, and deployment advisor grounded in real measured data.
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