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Build a hybrid cloud platform for AI/ML workloads

Leaders in a variety of industries are using the power of artificial intelligence (AI) and machine learning (ML) to deliver business outcomes.

Red Hat has the leading open hybrid cloud platform powered by containers, Kubernetes, DevOps, and a broad ecosystem of partner technologies to help you build a solid foundation for production-ready AI/ML environments, along with AI cloud services and training for rapid adoption.

Why AI/ML is important

Intelligent applications powered by AI and ML can help you accelerate business-critical initiatives. Many organizations are using AI/ML in production with intelligent applications that automatically discover, learn, and offer predictions and recommendations—gaining a competitive advantage and informing strategic planning.

Use cases include: autonomous driving, predictive maintenance, improved supply chains, improved risk analysis, reduced fraud, more personalized services, quicker research outcomes and disease identification and treatment, and protection of life and health of citizens via smart cities, weather and climate modeling, and population health.


Red Hat and partner capabilities drive AI/ML adoption

While there are many benefits, adopting AI/ML can pose significant challenges. In addition to hardware investments, there are architectural, cultural, and process considerations associated with creating an agile AI/ML environment. Proprietary solutions are complex and inflexible, making it increasingly difficult to integrate and operate AI/ML workloads at scale.

Open source containers and Kubernetes DevOps practices are key to accelerating the AI and ML life cycles and intelligent application delivery. These technologies give data scientists much-needed agility, flexibility, portability, and scalability to train, test, and deploy models in production.

A leader among multicloud container development platforms,1 Red Hat® OpenShift® enables collaboration between data scientists and software developers. It accelerates the rollout of intelligent applications across hybrid cloud environments, from the data center to the edge to multiple clouds.


Red Hat OpenShift Data Science

Provide data scientists and developers with a fully supported sandbox where they can rapidly develop, train, and test intelligent applications before deploying in production. Red Hat OpenShift Data Science platform can be extended with technology partners including Starburst, Anaconda, IBM, Seldon and NVIDIA.

Create a unified environment for application development, delivery, integration, and automation. Data integration services help you build effective data pipelines, runtime services simplify application development, and process automation streamlines and automates business processes and decisions.


Red Hat platform and storage products

Red Hat Enterprise Linux®, Red Hat Virtualization, Red Hat OpenStack® Platform, "Red Hat® OpenShift® Data Foundation, and Red Hat Ceph® Storage provide a scalable, software-defined infrastructure for hybrid operations.

The Red Hat certified partner ecosystem allows you to integrate your choice of AI/ML and application development tools into this architecture, enabling you to successfully adopt AI/ML for intelligent applications with better business results.

1 Bartoletti D, Dai C, et al., "The Forrester Wave™: Multicloud Container Development Platforms 2020 Q3 report," Forrester Research, September 2020.

Certified Software

Innovate with Red Hat's AI/ML certified partner solutions

Red Hat and our certified partners provide the solutions you need to accelerate AI/ML initiatives with confidence.

Customers value the broad partner ecosystem Red Hat has created around Red Hat Enterprise Linux® since 2002, which Red Hat has expanded to address additional enterprise needs for AI solutions running in containers on Kubernetes. These robust ecosystems demonstrate Red Hat’s long and ongoing commitment to delivering productive environments, populated by software partners.


AI Framework

AI/ML framework on Kubernetes

Red Hat works closely with an ecosystem of certified partners, who extend and enhance our portfolio of infrastructure solutions and end-to-end integration to provide tailored solutions that directly address your use cases and enable success.

A key part of accelerating the ML life cycle are Kubernetes Operators—a method of packaging, deploying, and managing a Kubernetes application that accompanies a partner’s solution. Red Hat OpenShift relies on operators to reduce operations effort and cost, increase service reliability, and spur innovation by freeing teams from repetitive maintenance work. Red Hat works closely with partners to meet key certification standards. Operator Hub, integrated into Red Hat OpenShift, provides easy access to fully certified operators from our partners that meet a high bar of quality standards.

Why deploy containers, Kubernetes, and DevOps for AI/ML?



Respond quickly with automated compute resource management and increased collaboration.


Consistency & portability

Develop and deploy models consistently across data center, edge, and public clouds.



Provision AI/ML environments when you need them.



Take advantage of autoscaling and high availability for the AI/ML solution stack.

Certified partner software solutions for AI/ML

Red Hat AI partners build on the Red Hat Infrastructure to complete and optimize AI/ML application development. They help to complete the AI pipeline with solutions ranging from data integration and preparation, to AI model development and training, to model serving and inferencing (making predictions) based on new data. All of this is supported on a self-service hybrid multicloud platform that empowers data scientists, data engineers, and software developers to be agile and collaborative, with a consistent experience across on-premises, public clouds, and edge locations.

Case studies

Customer success

Royal Bank of Canada boosts efficiency with an AI private cloud platform

Royal Bank of Canada (RBC) and its AI research institute, Borealis AI, partnered with Red Hat and NVIDIA to build a private cloud AI that could bring transformative intelligent applications to market faster and enhance the customer experience. With Red Hat OpenShift and NVIDIA’s DGX AI computing systems, RBC can run thousands of simulations and analyze millions of data points in a fraction of the time previously required.

Build a solid foundation for AI/ML

Explore the following Red Hat AI/ML ISV partners, or visit the ISV partner page to find certified and supported partner solutions across a range of other use cases.

Together, Red Hat and NVIDIA speed up delivery of AI-powered intelligent apps across data center, edge, and public clouds.

Red Hat OpenShift with CDP Private Cloud is a next-generation, on-premises data analytics platform that can transform complex data into clearer and more actionable insights.

Red Hat and SAS collaborate to enable organizations to use open, hybrid cloud technologies and analytical capabilities to drive business-level intelligence.

Starburst Enterprise for Presto on Red Hat OpenShift enables rapid analytics across multiple data platforms.

Red Hat OpenShift is optimized for H2O Driverless AI and H2O Open Source, making it easier to deploy and manage products across the hybrid cloud.

Seldon open source platforms make it easier and faster to deploy ML models and experiments at scale on Kubernetes with Red Hat OpenShift.

CognitiveScale collaborates with Red Hat to help enterprises use
AI to accelerate their digital transformation journeys and make the vision of open, scalable, and trusted AI a reality.

Anaconda and Red Hat address innovation in AI by bridging the gap between data science and IT in the enterprise to quickly deliver insights into the hands of decision makers.

PerceptiLabs works closely with Red Hat to create enterprise-ready ML tools that run on Red Hat OpenShift.

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We’re the world’s leading provider of enterprise open source solutions—including Linux, cloud, container, and Kubernetes. We deliver hardened solutions that make it easier for enterprises to work across platforms and environments, from the core datacenter to the network edge.

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