Red Hat OpenShift AI

Red Hat OpenShift AI is an artificial intelligence platform that runs on top of Red Hat OpenShift and provides tools across the AI/ML lifecycle.

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What’s new in Red Hat OpenShift AI

Red Hat OpenShift AI is an artificial intelligence (AI) platform that provides tools to rapidly develop, train, serve, and monitor machine learning models on-site, in the public cloud, or at the edge.

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KAFKA

Build smarter with Red Hat OpenShift AI

OpenShift AI gives data scientists and developers a powerful AI/ML platform for building AI-enabled applications. Data scientists and developers can collaborate to move from experiment to production in a consistent environment quickly.

OpenShift AI is available as an add-on cloud service to Red Hat OpenShift Service on AWS or Red Hat OpenShift Dedicated or as a self-managed software product. It provides an AI platform with popular open source tooling. Familiar tools and libraries like Jupyter, TensorFlow, and PyTorch along with MLOps components for model serving and data science pipelines are integrated into a flexible UI.

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Introducing Red Hat AI Inference Server

Deploy your preferred models faster and more cost-effectively across the hybrid cloud with Red Hat AI Inference Server. Its vLLM runtime maximizes inference throughput and minimizes latency. A pre-optimized model repository ensures rapid model serving, while the LLM compressor reduces compute costs without sacrificing accuracy. Experience fast, accurate inference for a wide range of applications.

Red Hat AI Inference Server is included in Red Hat OpenShift AI and Red Hat Enterprise Linux AI and supported on Red Hat OpenShift and Red Hat Enterprise Linux.

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Accelerate Data Science

Enabling rapid experimentation and model development on OpenShift allows developers to integrate models into their workflows with fewer obstacles.

Flexibility and choice

Flexibility and choice

Red Hat OpenShift AI is available as a managed cloud service or a traditional software product. Data scientists and developers can collaborate to develop, train, and deploy models in a common, trusted environment—whether on-site, in the cloud, or at the edge. Focus on application development and innovation rather than managing tickets and chasing down unsupported tools and patches.
Operationalize AI/ML models

Operationalize AI/ML models

With Red Hat's expertise in application platforms powered by Kubernetes, we provide capabilities to move your experimental models to production faster, with fewer obstacles. With tools like model serving, data science pipelines, and model monitoring, data scientists can use similar DevOps principles honed by application developers on the same OpenShift platform.
Choose your technology partners

Choose your technology partners

Choose from a broad range of validated partner data science and ML tools. Software and SaaS-based offerings from Starburst, IBM Watson, Intel, and Pachyderm are integrated directly into the UI, and dozens of other partner offerings are available on OpenShift.

Red Hat OpenShift AI is open source

Red Hat’s product development cycle has always been rooted in open source and the communities that help to steer Red Hat’s products’ direction. Like Fedora is the upstream project for Red Hat Enterprise Linux, the projects listed here are the upstream versions of products that make up Red Hat OpenShift AI.

Open Data Hub and Kubeflow installation customization

Open Data Hub

Red Hat OpenShift AI is based on the upstream project Open Data Hub, which is a blueprint for building an AI as a service platform on Red Hat's Kubernetes-based OpenShift Container Platform. Open Data Hub is a meta-project that integrates over 20 open source AI/ML projects into a practical solution. Red Hat OpenShift AI provides a subset of the tools offered in Open Data Hub as a commercially supported software product or cloud service.
Jupyter logomark

Jupyter

Project Jupyter is a project born out of the IPython Project in 2014 as it evolved to support interactive data science and scientific computing across all programming languages. Jupyter is a community of data enthusiasts who believe in the power of open tools and standards for education, research, and data analytics.
Tensorflow logomark

TensorFlow

TensorFlow is an end-to-end, open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML, and developers easily build and deploy ML-powered applications.
Pytorch logomark

PyTorch

PyTorch is an open source machine learning framework that accelerates the path from research prototyping to production deployment. It is used for applications such as computer vision and natural language processing.
SciKit logomark

Scikit-learn

Scikit-learn is a machine learning library for Python. It is built on Numpy, Scipy, and Matplotlib and offers simple and efficient tools for predictive data analysis.
Kubeflow

Kubeflow

Kubeflow is an open source framework aimed at simplifying AI/ML workflow deployment at scale. Red Hat OpenShift AI integrates the Kubeflow notebook controller, model serving, and data science pipeline components into the core product.