Red Hat AI

In this video, Maarten demonstrates a neural network and how it works in AI/ML models. Neural networks are a class of ML models inspired by the human brain, made up of interconnected units of neurons, or nodes. Neural networks are the foundation of many AI applications, including image recognition, speech processing, and natural language understanding.

In this recording, we demonstrate how to compose model compression experiments, highlighting the benefits of advanced algorithms requiring custom data sets and how evaluation results and model artifacts can be shared with stakeholders.

Podman enables developers to run Linux containers on MacOS within virtual machines, including GPU acceleration for improved AI inference performance.

Learn how to control the output of vLLM's AI responses with structured outputs. Discover how to define choice lists, JSON schemas, regex, and more.

Headed to WeAreDevelopers World Congress 2025? Visit the Red Hat Developer booth on-site to speak to our expert technologists.

Explore how to utilize guardrails for safety mechanisms in large language models (LLMs) with Node.js and Llama Stack, focusing on LlamaGuard and PromptGuard.

Learn how to optimize GPU resource use with NVIDIA Multi-Instance GPU (MIG) and discover how MIG-Adapter enhances GPU resource utilization in Kubernetes.

Members from the Red Hat Node.js team were recently at PowerUp 2025. It was held

Discover how IBM used OpenShift AI to maximize GPU efficiency on its internal AI supercomputer, using open source tools like Kueue for efficient AI workloads.

Gain detailed insights into vLLM deployments on OpenShift AI. Learn to build dashboards with Dynatrace and OpenTelemetry to enable reliable LLM performance.

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

Explore the complete machine learning operations (MLOps) pipeline utilizing Red

llm-d delivers Kubernetes-native distributed inference with advanced optimizations, reducing latency and maximizing throughput.

LLM Semantic Router uses semantic understanding and caching to boost performance, cut costs, and enable efficient inference with llm-d.

Optimize model inference and reduce costs with model compression techniques like quantization and pruning with LLM Compressor on Red Hat OpenShift AI.

Learn how to use synthetic data generation (SDG) and fine-tuning in Red Hat AI to customize reasoning models for your enterprise workflows.

Learn how to deploy a trained model with Red Hat OpenShift AI and use its

Explore how to use large language models (LLMs) with Node.js by observing Ollama

More Essential AI tutorials for Node.js Developers

Learn how to run a fraud detection AI model using confidential virtual machines on RHEL running in the Microsoft Azure public cloud.

Configure your Red Hat Enterprise Linux AI machine, download, serve, and

vLLM empowers macOS and iOS developers to build powerful AI-driven applications by providing a robust and optimized engine for running large language models.

PowerUP 2025 is the week of May 19th. It's held in Anaheim, California this year

Learn how to use pipelines in OpenShift AI to automate the full AI/ML lifecycle on a single-node OpenShift instance.

Jupyter Notebook works with OpenShift AI to interactively classify images. In