Artificial intelligence

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

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.

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.

Quantized LLMs achieve near-full accuracy with minimal trade-offs after 500K+ evaluations, providing efficient, high-performance solutions for AI model deployment.

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

Machete, Neural Magic’s optimized kernel for NVIDIA Hopper GPUs, achieves 4x memory savings and faster LLM inference with mixed-input quantization in vLLM.

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.