Red Hat, Inc., a global provider of open source solutions, has announced the availability of Red Hat OpenShift Data Science as a field trial, as well as an expanded partner ecosystem focused on this new cloud service offering.
As leading artificial intelligence and machine-learning (AI/ML) partners support the service, Red Hat customers are provided with a range of solutions optimized for Red Hat OpenShift, letting them select the technologies to best meet their specific machine learning needs across the open hybrid cloud and edge computing environments.
Red Hat OpenShift Data Science is a cloud service offering tailored for machine learning (ML) on Red Hat OpenShift, the industry’s leading enterprise Kubernetes platform. Enabled by Kubernetes operators, Red Hat OpenShift Data Science gives enterprises greater flexibility in selecting the technologies to develop, test and deploy ML models, while removing the overhead associated with running and maintaining a production platform. As a fully-managed cloud service, Red Hat OpenShift Data Science moves operational responsibility and support to Red Hat. This gives organizations the freedom to use their chosen AI/ML tools in developing the next-generation of intelligent applications to drive valuable business insights.
Several key members of Red Hat’s AI/ML partner ecosystem are now pre-integrated into the user interface dashboard, providing access to the latest in hardware and software acceleration solutions as well as tools to support the model operationalization lifecycle. This includes Intel toolkits and, planned in the near future, NVIDIA-accelerated computing support, which will enable customers to benefit from:
Access to a fully-integrated model development environment, with the ability to optimize and tailor model behavior on Intel hardware, using Intel OpenVINO Pro for Enterprise; and accelerated speed to insights with the Intel oneAPI AI Analytics Toolkit, which provides data scientists with a series of tools and frameworks optimized for maximum performance on Intel-based CPUs. With these offerings from Intel, data scientists not only have integrated access to the tools and frameworks needed to build and deploy their model, but also help deliver high performance on Intel hardware.
Accelerated computing support through the use of NVIDIA GPU technology, which can enable data scientists to scale their computationally expensive neural networks to large, complex architectures without sacrificing productivity. This allows data scientists to cut down the time spent on training models with minimal code changes.
In addition to Intel and NVIDIA, Red Hat OpenShift Data Science includes support from several other leading AI/ML partners, including:
Anaconda Commercial Edition for more secure, consistent and repeatable data science package distribution and management;
IBM Watson Studio with AutoAI to build, run, and manage AI models at scale;
Seldon Deploy to simplify and accelerate deploying, managing and monitoring machine learning models, and
Starburst Galaxy to unlock the value of your data by making it faster and easier to access data across the hybrid cloud.
As enterprises invest in AI/ML to drive business decisions and gain actionable insights from data, we have seen a highly-competitive marketplace for MLOps solutions that strive to operationalize the ML lifecycle. Red Hat OpenShift Data Science offers enterprise customers a customizable alternative to prescriptive AI/ML solutions, through its open workflow platform including Jupyter notebooks and common frameworks including Pytorch and Tensorflow, and complemented by access to certified partner technology from Red Hat Marketplace.
Red Hat OpenShift Data Science is available via field trial today as an add-on to Red Hat OpenShift Dedicated and on Red Hat OpenShift Service on AWS. The field trial release of Red Hat OpenShift Data Science offers general availability-level quality and support, allowing customers to try out the service, while only paying for the underlying Red Hat OpenShift Dedicated or Red Hat OpenShift Service on AWS and AWS infrastructure.
Mike Piech, vice president and general manager, Cloud Data Services, Red Hat, said, “Data science and machine learning are helping drive innovation and business value in nearly every industry. For many companies the biggest barrier to adoption is the complexity of wiring together the necessary data sources with diverse model training and model deployment technologies. With Red Hat OpenShift Data Science, Red Hat’s contributions to Open Data Hub, and our extensive partner ecosystem, we’re helping organizations overcome such complexity to begin harnessing the full potential of machine learning from the leader in trusted open source technology.”
Stephen Nolan, head of product, Anaconda, commented, “When developing Red Hat OpenShift Data Science, Red Hat recognized the importance of making it easier for organizations to use tools they know and trust. Anaconda is excited to offer Anaconda Commercial Edition as part of the platform so that customers can leverage the innovation of open source packages from a trustworthy, premium repository.”
Hemanth Manda, director, Cloud Pak for Data, IBM, explained, “Leveraging AI to guide business decision-making is more relevant than ever, especially in the scale of enterprise readiness. Red Hat OpenShift Data Science enables data scientists and developers to work in an open and flexible environment and is complemented by Watson Studio from IBM Cloud Pak for Data – an industry-leading offering that accelerates the data science lifecycle at scale. With time and reliability being a valuable resource, IBM Watson Studio infuses AutoAI, which accelerates AI model development through automation, and Trusted AI – offering enterprises trust and transparency so that their models can be governed, accurate, and free of bias. We’re excited to complement IBM’s enterprise-readiness with the open-source capabilities of Red Hat to provide customers a convenient and trusted pathway to their adoption of AI.”
Wei Li, vice president and general manager of Artificial Intelligence and Analytics, Intel, said, “Red Hat OpenShift Data Science provides a solution to data scientists and developers who want flexibility and control of their resources and data, but no longer want to deal with the complexities of managing infrastructure. That is why Intel is very proud to partner with Red Hat to offer the Intel® oneAPI AI Analytics Toolkit and OpenVINO™ Toolkit as part of this platform.”
Scott McClellan, head of data science products, NVIDIA, said, “Data scientists and AI developers require well-integrated tools and powerful computing capabilities to do their best work. NVIDIA and Red Hat’s collaboration to bring accelerated data science to Red Hat OpenShift platforms can enable enterprises to rapidly develop, deploy and scale advanced AI across a broad variety of workloads and industries.”
Keith Bush, vice president, Alliances, Seldon, commented, “When it comes to enterprise-wide adoption of AI and ML, organisations want the freedom and flexibility to leverage the best tools available – regardless of vendor. Red Hat’s strategy in developing Red Hat OpenShift Data Science reflects this modern approach, and we’re excited to offer Seldon Deploy as a complementary solution to this cloud service. By offering customers the ability to easily deploy, manage, and monitor their ML models, Seldon empowers data scientists and DevOps teams by eliminating the typical bottlenecks and delays associated with moving models into production. Our solution is specifically designed for rapid model deployment in an enterprise environment such as Red Hat OpenShift Data Science, with advanced tools and capabilities surrounding testing, explainability, and lifecycle management.”
Justin Borgman, CEO, co-founder and chairman, Starburst, said,
“We’re excited to be one of the key partners featured in Red Hat OpenShift Data Science. Starburst provides a new way to conduct analytics via a single point of data access built to offer greater security, minimizing the requirement to move or copy data in order to analyze it. Together, our technologies provide data scientists with a fully managed, enterprise-grade platform to more seamlessly and quickly build, train, test, and deploy new machine learning models that can enable organizations to make faster and better decisions based on more complete and accurate data.”