AI/ML

This tutorial demonstrates how to use Jupyter Notebooks within Red Hat OpenShift

LLM fine-tuning is the process of adjusting a pre-trained Large Language Model

Learn the basics of Kubernetes, Ansible, AI, and more with these popular learning paths, and get hands-on experience using the no-cost Developer Sandbox.

This year's top articles on AI include an introduction to GPU programming, a guide to integrating AI code assistants, and the KServe open source project. 

In our previous blog post, we introduced the RamaLama project, a bold initiative

The Red Hat Developer Hub team recently joined the vibrant Backstage community

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.

Come and join us for our year-end review as we enjoy the company of a few guests, discuss things that happened in 2024, and talk about what we think 2025 will bring.

Feel free to bring your topics to the discussion and we’ll make sure to ask the guests what their thoughts are on your favorite topics.

Meet some of the winners from the 2024 Red Hat and Intel AI Hackathon. They will review their Gen AI or an AI Retrieval Augmented Generation (RAG) application built on the Red Hat OpenShift AI environment on AWS with Intel’s Xeon Processor AMX features. Learn more about their process and how they did this!

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

Using a chatbot to call a Node.js function inside a Large Language Model.

Download this 15-page e-book to explore 5 key ways OpenShift benefits developers, including integrated tools and workflows and simplified AI app development.

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

The RamaLama project simplifies AI model management for developers by using OCI containers to automatically configure and run AI models.

This article details new Python performance optimizations in RHEL 9.5.

This guide walks through how to create an effective qna.yaml file and context file for fine-tuning your personalized model with the InstructLab project.

A list of curated posts of essential AI tutorials from our Node.js team at Red

Discover how you can use RHEL AI to fine-tune and deploy Granite LLM models, leveraging techniques like retrieval-augmented generation and model fine-tuning.

Download a free preview of Applied AI for Enterprise Java Development (O’Reilly), a practical guide for Java developers who want to build AI applications.

Learn how to use Red Hat OpenShift AI to quickly develop, train, and deploy

Learn how to configure Testing Farm as a GitHub Action and avoid the work of setting up a testing infrastructure, writing workflows, and handling PR statuses.

Red Hat was recently at NodeConf EU, which was held on November 4-6th 2024. This

Improving Chatbot result with Retrieval Augmented Generation (RAG) and Node.js

Discover how to integrate NVIDIA NIM with Red Hat OpenShift AI to create and deliver AI-enabled applications at scale.

Cataloging AI assets can be useful for platform engineers and AI developers. Learn how to use Red Hat Developer Hub to catalog AI assets in an organization.