Develop, train, test, and deploy ML across the hybrid cloud

Artificial intelligence, machine learning, and deep learning (AI/ML/DL) have rapidly become critical for businesses and organizations with adoption of generative AI (gen AI) increasing rapidly. Gartner now estimates that 85% of enterprises will have used gen AI application programming interfaces (APIs) or deployed gen AI-enabled applications by 2026.¹ 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. These gaps can negatively impact productivity and collaboration among data scientists, software developers, and IT operations. Scaling AI/ML deployments can be resource-limited and administratively complex while requiring expensive graphics processing unit (GPU)-resources for hardware acceleration and distributed workloads for gen AI. Popular cloud platforms offer scalability and attractive toolsets, but those same tools often lock users in, limiting architectural and deployment choices.

Based on the open source Open Data Hub project, Red Hat® OpenShift® AI² lets data scientists rapidly train, test, serve and monitor ML/DL models including gen AI. Users can immediately focus on their modeling and application development without waiting for infrastructure provisioning. Available as an add-on to Red Hat OpenShift, either as a fully managed cloud service or as a self-managed software product, OpenShift AI combines Red Hat components, open source software, and technology partner offerings with the flexibility to develop and serve models on-premise, in a cloud, or on edge infrastructure.

OpenShift AI offers organizations an efficient way to deploy an integrated set of common open source and third-party tools to perform AI/ML modeling. The platform 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 capabilities. Models can be served to production environments in a container-ready format, consistently, across hybrid cloud and edge environments. OpenShift AI provides IT operations with an environment that is simple to manage, with straightforward configurations on a proven, scalable, and security-focused platform.

OpenShift AI supports popular gen AI foundation models, letting you prompt-tune, fine tune, and serve these pretrained models for your unique use cases and with your own data. You can even distribute workloads across multiple Red Hat OpenShift clusters, independent of their location. The platform makes it simpler to exploit AI hardware acceleration too, supporting central processing unit (CPU) and graphic processing unit (GPU)-based hardware infrastructure including Nvidia GPUs and Intel XPUs—all without the need to stand up and manage your own data science platform. 

Red Hat OpenShift AI provides a subset (Table 1) of the tools found in the upstream Open Data Hub project. Organizations can develop, test, and deploy models across any cloud environment, fully managed, and self-managed Red Hat OpenShift and centrally monitor their performance. Red Hat provides regular updates to open source tools (e.g., Jupyter, Pytorch, and Tensorflow), removing integration, testing and maintenance burden. The offering also integrates several AI/ML technology partner offerings (Table 1). Additional commercial technology partner offerings can also be added from more than 30 AI technology partners who have certified their products on Red Hat OpenShift.

Table 1 Red Hat OpenShift AI ecosystem