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Red Hat OpenShift Data Science

Accelerate AI/ML adoption with Red Hat OpenShift Data Science

Artificial intelligence (AI), machine learning (ML), and deep learning (DL)  have rapidly become critical for businesses and organizations. According to IDC, "AI is profound and is impacting businesses and organizations across industries. AI is everywhere across the technology stack."1 Deploying these technologies, however, can be complicated. As data scientists strive to build their models, they often encounter a lack of alignment between rapidly evolving tools, impacting productivity and collaboration among themselves, software developers, and IT operations. Scaling AI/ML deployments can be resource-limited and administratively complex while requiring expensive GPU resources for hardware acceleration. Popular cloud platforms offer scalability and attractive toolsets, but those same tools often lock users in, limiting architectural and deployment choices.

Red Hat® OpenShift® Data Science is a managed cloud service offering based on the open source Open Data Hub project. Inheriting capabilities from upstream efforts—such as Apache Kafka, Strimzi, and Kubeflow—Open Data Hub provides an architecture for building an AI-as-a-Service platform on Red Hat OpenShift and Ceph object storage. 

With this solution, data scientists and developers can rapidly develop, train, test, and iterate ML and DL models in a fully supported sandbox environment—without waiting for infrastructure provisioning. Available as an add-on to Red Hat OpenShift Dedicated and Red Hat OpenShift Service on AWS, OpenShift Data Science combines Red Hat components, open source software, and certified partner technology from Red Hat Marketplace with the public cloud scalability of Amazon Web Services (AWS).

Develop, model, experiment, and deploy AI/ML more rapidly

Red Hat OpenShift Data Science offers organizations a way to rapidly deploy an integrated set of common open source and third-party tools to perform AI/ML modeling. The platform makes it simpler to exploit NVIDIA GPU-enabled hardware infrastructure without the need to stand up and perform daily management of Kubernetes on your own.

Red Hat OpenShift Data Science represents an alternative to prescriptive and opinionated AI/ML suites available from individual cloud providers. Adopters gain a collaborative open source toolset and a platform for building experimental models without worrying about the infrastructure or lock-in from public cloud-specific tools. They can then extend that base platform with partner tools to gain increased capability. Models can be exported to production environments in a container-ready format, consistently, across hybrid cloud and edge environments.

Red Hat OpenShift Data Science supports rapid experimentation with user-supplied data where the model outputs are either:

  • Hosted on OpenShift Dedicated or OpenShift Service on AWS for integration into a customer-defined intelligent application.
  • Exported for hosting in your environment.

Customize your environments with popular open source and commercial tools

Red Hat OpenShift Data Science provides a subset (Table 1) of the 20+ tools found in the upstream Open Data Hub project. Offering this service on Red Hat OpenShift means that organizations don’t have to worry about deploying Kubernetes themselves or purchasing and installing NVIDIA graphics processing units (GPUs) for on-premise deployments. Red Hat provides regular updates to open source tools (e.g., Jupyter, Pytorch, and Tensorflow) through the OpenShift managed cloud service, removing integration and testing burden. The offering also integrates several AI/ML technology partner offerings (Table 1). Additional commercial technology partner offerings can also be added from Red Hat Marketplace.

Table 1 Initial Red Hat OpenShift Data Science ecosystem


  • Rapidly develop, train, test, and deploy ML models in the cloud, without designing and deploying Kubernetes infrastructure.
  • Conduct exploratory data science in Jupyter Notebooks with access to core AI/ML libraries and frameworks, including TensorFlow and PyTorch.
  • Publish models as end points using the Source-to-Image (S2I) tool for integration with intelligent apps. Rebuild and deploy based on changes to the source notebook.
  • Streamline model training and workflows using on-demand NVIDIA GPU acceleration.

Open Data Hub is a blueprint for building an AI-as-a-Service (AIaaS) platform on Red Hat OpenShift and Red Hat Ceph® Storage. It inherits from upstream efforts such as Kafka Strimzi and Kubeflow, and is the foundation for Red Hat’s internal data science and AI platform.

Red Hat Consulting offers several AI/ML consulting engagements for challenges like implementing MLOps and intelligent application development.

AI/ML modeling and visualizationJupyter Hub with predefined notebook images;  TensorFlowPyTorchAnaconda (Commercial Edition is optional); Intel AI Analytics Toolkit (AI Kit)
IBM Watson Studio (optional)
Data engineeringStarburst (Galaxy is optional); Pachyderm (optional)
Data ingestion and storageRed Hat OpenShift Streams for Apache Kafka (optional), (OpenShift Dedicated optional add-on); Amazon Simple Storage Service (S3)
GPU supportNVIDIA (with GPU operator)
Model servingSource-to-Image (OpenShift), Intel Distribution of the OpenVINO toolkit

Support your AI/ML adoption with Red Hat Consulting experts

Meet challenges like adoption of DevOps and ML best practices with Red Hat Consulting. See how an AI/ML residency with Red Hat consultants and technology experts through Red Hat Open Innovation Labs can help you succeed with Red Hat OpenShift Data Science. Read the brief to learn more about Open Innovation Labs for AI/ML adoption.

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