AI/ML

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 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.

Learn how to use Red Hat Developer Hub to easily create and deploy applications to your image repository or a platform like Red Hat OpenShift AI.

Use AI and Node.js to generate a JSON response that contains a summarized email

Learn how to safely deploy and operate AI services without compromising on...

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

Learn how to set up a cloud development environment (CDE) using Ollama, Continue

Podman Desktop provides a graphical interface for application developers to work seamlessly with containers and Kubernetes in a local environment.

In this course, you’ll explore how to configure your RHEL AI machine, download

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.

Announcing the General Availability of Red Hat Enterprise Linux AI (RHEL AI)

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.

This tutorial gives you a unique chance to learn, hands-on, some of the basics of large language models (LLMs) in the Developer Sandbox for Red Hat OpenShift.

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