Red Hat OpenShift AI

Download Red Hat software for application developers at no-cost.

Explore how to use OpenVINO Model Server (OVMS) built on Intel's OpenVINO toolkit to streamline the deployment and management of deep learning models.

End-to-end AI-enabled applications and data pipelines across the hybrid cloud

Over 80% of enterprises will have used generative AI (gen AI) APIs or deployed generative AI-enabled applications by 2026, according to Gartner. The barriers for joining these enterprises and integrating generative AI into the application development process are lower than ever. No need for extra funding or complex environments, just the know-how this video provides.

Learn a simplified method for installing KServe, a highly scalable and standards-based model inference platform on Kubernetes for scalable AI. 

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

Dive into the end-to-end process of building and managing machine learning (ML)

This guide will walk you through the process of setting up RStudio Server on Red Hat OpenShift AI and getting started with its extensive features.

Are you curious about the power of artificial intelligence (AI) but not sure

The Edge to Core Pipeline Pattern automates a continuous cycle for releasing and deploying new AI/ML models using Red Hat build of Apache Camel and more.

Explore the fundamental concepts of artificial intelligence (AI), including machine learning and deep learning, and learn how to integrate AI into your platforms and applications.

Create intelligent, efficient, and user-friendly experiences by integrating AI

Learn how to deploy a trained AI model onto MicroShift, Red Hat’s lightweight Kubernetes distribution optimized for edge computing.

Accurately labeled data is crucial for training AI models. Learn how to prepare and label a custom dataset using Label Studio in this tutorial.

Learn how to configure Red Hat OpenShift AI to train a YOLO model using an already provided animal dataset.

Learn how to install the Red Hat OpenShift AI operator and its components in this tutorial, then configure the storage setup and GPU enablement.

Learn how to deploy single node OpenShift on a physical bare metal node using the OpenShift Assisted Installer to simpify the OpenShift cluster setup process.

Learn how to create a Red Hat OpenShift AI environment, then walk through data labeling and information extraction using the Snorkel open source Python library.

Learn how to access a large language model using Node.js and LangChain.js. You

Discover the benefits of KServe, a highly scalable machine learning deployment tool for Kubernetes.

Learn how Intel Graphics Processing Units (GPUs) can enhance the performance of machine learning tasks and pave the way for efficient model serving.

Discover how to integrate cutting-edge OpenShift AI capabilities into your Java applications using the OpenShift AI integration with Quarkus.

Discover how to use machine learning techniques to analyze context, semantics, and relationships between words and phrases indexed in Elasticsearch.

Walk through the basics of fine-tuning a large language model using Red Hat OpenShift Data Science and HuggingFace Transformers.

Learn how to use the Red Hat OpenShift Data Science platform and Starburst to develop a fraud detection workflow with an AI/ML use case.