Myriad organizations are embracing artificial intelligence and machine learning (AI/ML) to improve the customer experience, make employees more productive, accelerate innovation, and develop new products with increased speed. Generative AI adoption is increasingly mainstream, with developers across all domains and industries exploring ways to benefit from these foundational models. Despite these attractive benefits and significant industry-wide investments, a high AI/ML project failure rate remains, with many experiencing real challenges moving projects from concept to production.1 These challenges are understandable, as data science encompasses many tasks—data acquisition, model building, development processes, and continuous integration and continuous delivery (CI/CD), to name a few.

With more than 25 years of working together, Intel® and Red Hat can help organizations face these challenges directly and rapidly operationalize their AI/ML models. Red Hat® OpenShift® AI provides an AI-focused portfolio with tools that span the entire life cycle of AI/ML experiments and models. In collaboration with Red Hat, Intel furnishes proven, optimized libraries and toolkits, including Intel oneAPI AI Analytics Toolkit (AI Kit) and OpenVINO Pro for Enterprise. These products equip data scientists with the latest tools and technologies optimized for Intel architecture-based hardware, including Intel Xeon processors, Habana Gaudi for dedicated deep-learning training and inference, Intel Data Center GPU Flex Series for AI inference, and Intel Data Center GPU Max Series for AI inferencing and training. 

Key benefits

Coupled with Intel software and hardware offerings, Red Hat OpenShift AI brings key benefits to data scientists and development teams, including:

• Eliminating complex ITOps setup tasks.
• Managing software life cycles efficiently through a choice of a managed cloud service or traditional software deployment methods.
• Providing specialized components and integrated independent software vendor (ISV) support within the user interface (UI).
• Allowing development and deployment on any environment across the datacenter, the edge, and public clouds.
• Serving models and automating data science pipelines at scale to simplify MLOps processes