Rapidly develop, train, test, and deploy
OpenShift Data Science is based on the community Open Data Hub project and Operate First. Open Data Hub demonstrates an AI/ML platform on Red Hat OpenShift with upstream efforts such as Apache Kafka and Kubeflow. Operate First brings open source concepts to operations, letting developers and operators collaborate to infuse operational excellence, without proprietary lock-in. OpenShift Data Science provides a subset of Open Data Hub tools in a fully supported cloud service, managed on Amazon Web Services (AWS) with optional independent software vendor (ISV) offerings.
Experiment with a choice of tools
With OpenShift Data Science, data scientists can experiment and discover new ways of bringing insights into the business. As a fully managed cloud service, data scientists can develop, train, and test machine learning models before they deploy. Teams get access to advanced tools delivered in an integrated experience. Data scientists can use their familiar tools or access a growing technology partner ecosystem for deeper AI/ML expertise—all without being burdened with a prescriptive toolchain. Rather than waiting for IT to provision necessary resources, they get on-demand infrastructure with a click rather than an IT ticket.
Collaborate on a common platform
OpenShift Data Science builds on an open source architecture designed for machine learning workloads and development workflows. It narrows the gaps between data science and DevOps, reducing the pain of handoffs on the way to production. Data scientists collaborate in real time in Jupyter notebooks. Developers integrate container-ready models into intelligent applications with less friction. IT worries less about governance, with no need to chase down rogue cloud-platform accounts.
Accelerate speed to market for intelligent applications
OpenShift Data Science brings machine learning models from early pilots into intelligent applications with greater speed on a shared, consistent platform. Data scientists can start fast with their choice of tools and access to self-service infrastructure. The service connects every machine learning life cycle stage with deeper AI capabilities through its software partner ecosystem, offering a wide range of certified tools with specialty AI/ML expertise. You can deploy models to hybrid cloud environments, gaining the flexibility of running workloads wherever you need them, with no commercial cloud lock-in.