Join us for an afternoon filled with discussion and innovative technology as we explore the role that open source and Kubernetes plays for AI. We’ll explore the strengths and challenges that open source innovation brings to the enterprise and look at an opinionated approach to getting AI into production by Red Hat and Starburst.
What's behind the cycle?
In the ever-evolving AI landscape, the focus has transitioned from model creation to operationalizing AI, ensuring models seamlessly integrate and perform in real-world environments. Platforms like Red Hat OpenShift Data Science and Open Data Hub, foundational in developing and deploying AI/ML workloads, are at the forefront of this change. This presentation will delve into three pivotal post-hype cycle topics:
The imperatives of operationalizing AI, emphasizing the role of CI/CD, real-time performance monitoring, and integration within IT infrastructures.
The increasing importance of hardware accelerators, extending beyond current hardware-partners, and the need for platform support and workload optimization.
The urgent call for trustworthy and explainable AI, ensuring AI decisions are transparent, unbiased, and comprehensible.
Distributed Workloads on Elastic Clusters: Harnessing the power of OpenShift's Kubernetes foundation to dynamically allocate resources, ensuring optimal performance and efficiency.