What's new in OpenShift Data Science?
Red Hat OpenShift Data Science 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.
4 reasons you'll love using Red Hat OpenShift Data Science
Red Hat OpenShift Data Science is a managed cloud service built from a curated set of components where data scientists can develop, train, and test their machine learning (ML) workloads and then deploy results in a container-ready format.
Take your development environments to the cloud and build better projects.
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
Red Hat OpenShift Data Science 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
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 from a broad range of validated partner data science and ML tools. Software and SaaS-based offerings from Starburst, Anaconda, IBM Watson, Intel, and Pachyderm are integrated directly into the UI, and dozens of other partner offerings are available on OpenShift.
OpenShift Data Science 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 Data Science.
Red Hat OpenShift Data Science 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 Data Science provides a subset of the tools offered in Open Data Hub as a commercially supported software product or cloud service.
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 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 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-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 is an open source framework aimed at simplifying AI/ML workflow deployment at scale. OpenShift Data Science integrates the Kubeflow notebook controller, model serving, and data science pipeline components into the core product.