Red Hat OpenShift AI

Find Kubernetes and OpenShift articles on performance and scale testing, single-node OpenShift, OpenShift Virtualization for VMware vSphere admins, and more.

Join us as we get ready for the holidays with a few  AI holiday treats! We will demo AI from laptop to production using Quarkus and LangChain4j with ChatGPT, Dall-E, Podman Desktop AI and discover how we can get started with Quarkus+LangChain4j, use memory, agents and tools, play with some RAG features, and test out some images for our holiday party.

Learn how a developer can work with RAG and LLM leveraging their own data chat for queries.

Explore the evolution and future of Quarkus, Red Hat’s next-generation Java framework designed to optimize applications for cloud-native environments.

A practical example to deploy machine learning model using data science...

Repo, Red Hat Developer's new mascot, is curious, helpful, and eager to guide

Artificial intelligence (AI) and large language models (LLMs) are becoming

The rapid advancement of generative artificial intelligence (gen AI) has unlocked incredible opportunities. However, customizing and iterating on large language models (LLMs) remains a complex and resource intensive process. Training and enhancing models often involves creating multiple forks, which can lead to fragmentation and hinder collaboration.

OCI images are now available on the registries Docker Hub and Quay.io, making it even easier to use the Granite 7B large language model (LLM) and InstructLab.

Get started with AMD GPUs for model serving in OpenShift AI. This tutorial guides you through the steps to configure the AMD Instinct MI300X GPU with KServe.

Learn how developers can use prompt engineering for a large language model (LLM) to increase their productivity.

Learn how to deploy a coding copilot model using OpenShift AI. You'll also discover how tools like KServe and Caikit simplify machine learning model management.

Explore AMD Instinct MI300X accelerators and learn how to run AI/ML workloads using ROCm, AMD’s open source software stack for GPU programming, on OpenShift AI.

Learn how to apply supervised fine-tuning to Llama 3.1 models using Ray on OpenShift AI in this step-by-step guide.

Understand how retrieval-augmented generation (RAG) works and how users can

Learn how to rapidly prototype AI applications from your local environment with

Learn how to generate word embeddings and perform RAG tasks using a Sentence Transformer model deployed on Caikit Standalone Serving Runtime using OpenShift AI.

In today's fast-paced IT landscape, the need for efficient and effective

As enterprises seek to utilize the power of generative AI, they often struggle with the transition from experimentation to production-ready solutions. This talk will guide you through the steps needed to build an enterprise-grade generative AI application, starting with local experimentation using Podman AI Lab and finishing in a scalable, secure deployment on Red Hat OpenShift AI.

Ollama recently announced tool support and like many popular libraries for using

Use the Stable Diffusion model to create images with Red Hat OpenShift AI running on a Red Hat OpenShift Service on AWS cluster with NVIDIA GPU enabled.

Get an overview of Explainable and Responsible AI and discover how the open source TrustyAI tool helps power fair, transparent machine learning.

In this blog we look at how we use OpenShift AI with Ray Tune to perform

Red Hat OpenShift AI is an artificial intelligence platform that runs on top of Red Hat OpenShift and provides tools across the AI/ML lifecycle.

Red Hat OpenShift AI is an artificial intelligence platform that runs on top of Red Hat OpenShift and provides tools across the AI/ML lifecycle.