Red Hat OpenShift AI

BERT, which stands for Bidirectional Encoder Representations from Transformers

This article explains how to use Red Hat OpenShift AI in the Developer Sandbox for Red Hat OpenShift to create and deploy models.

Explore large language models (LLMs) by trying out the Granite model on Podman AI Lab.

This article demonstrates how to register the SKlearn runtime as a Custom ServingRuntime, deploy the iris model on KServe with OpenDataHub, and apply authentication using Authorino to protect the model endpoints.

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.

A common platform for machine learning and app development on the hybrid cloud.

Applications based on machine learning and deep learning, using structured and unstructured data as the fuel to drive these applications.

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