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Red Hat OpenShift Data Science

Red Hat® OpenShift® Data Science is a managed cloud service for data scientists and developers of intelligent applications. It provides a fully supported sandbox in which to rapidly develop, train, and test machine learning (ML) models in the public cloud before deploying in production.

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Accelerate your data science

Red Hat OpenShift Data Science enables companies to solve critical business challenges by providing a fully managed cloud service environment on Red Hat OpenShift Dedicated or Red Hat OpenShift Service on AWS

Red Hat OpenShift Data Science allows organizations to quickly build and deploy artificial intelligence (AI)/ML models by integrating open source applications with commercial partner technology.

The ML models built in Red Hat OpenShift Data Science are easily portable to other platforms, allowing teams to deploy them in production, on containers, and in the hybrid cloud.

OpenShift Data Science allows you to:

  • Quickly build experimental models with Jupyter notebooks, TensorFlow, PyTorch, and NVIDIA® GPU support without worrying about the underlying infrastructure. 
  • Consistently export models to production in a container-ready format across hybrid cloud and edge environments.
  • Build a platform tailored to your needs by choosing from over 30 leading AI/ML partner offerings through Red Hat Marketplace.
  • Develop best practices to accelerate AI/ML projects across hybrid clouds with Red Hat’s consulting services, including Open Innovation Labs.

Features & benefits

Fully managed, efficient cloud service

Through this AI/ML add-on service to Red Hat OpenShift Dedicated or Red Hat OpenShift Services for AWS, your data science teams can start their projects faster. Instead of standing up and managing your own Kubernetes infrastructure, you can focus on deploying intelligent applications, integrating the models you develop. Gain added benefits like security and operator life cycle integration, built into intelligent applications to simplify the deployment and maintenance of your models.

Tested, supported AI/ML tooling

Get quick updates and support for core open source tooling. Red Hat tracks, integrates, tests, and supports common AI/ML tooling like JupyterHub, TensorFlow, PyTorch, and the source-to-image framework on our Red Hat OpenShift cloud service, so you and your data scientists do not have to. OpenShift Data Science incorporates more than 30 AI/ML open source technologies, and draws from 4 years of incubation in Red Hat’s Chief Technology Officer’s Open Data Hub community project..

Choose your technology partners

Extend the core Red Hat OpenShift Data Science platform with other integrated Red Hat services like Red Hat OpenShift Streams for Apache Kafka and several leading AI/ML software technology partners including Starburst, Anaconda, IBM, and Seldon through Red Hat Marketplace.

Start fast and scale quickly

Give your data scientists extra resources to get started and scale quickly, like integrated GPU support with NVIDIA and CUDA runtimes.

Develop best practices

Unite your disparate operations teams and data scientist teams through Red Hat’s AI/ML Open Innovation Lab consulting services. Learn best practices and build your own sample ML pipeline project using DevOps and MLOps through Red Hat’s proven methodology.

Use cases

Collaborating through Jupyter notebooks-as-a-service

Engage a managed cloud service to build experimental models using JupyterHub. Red Hat OpenShift Data Science tracks changes to JupyterLab, TensorFlow, and PyTorch, integrating them into the service faster so you can speed up innovation.

Operationalizing AI/ML with Red Hat OpenShift

As the Kubernetes expert, Red Hat helps you easily integrate your data science projects into intelligent applications for hybrid cloud deployment.

Enable integrations with Red Hat’s AI/ML partner ecosystem

Red Hat OpenShift Data Science integrates open source tools with Red Hat's AI/ML partner ecosystem to accelerate project development and enhance platform capabilities. With Red Hat's hybrid cloud approach you can eliminate cloud provider lock-ins and minimize vendor lock-ins.

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