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Training and Inferencing at Scale, Across Node and Cluster Borders with Optimized Software and Hardware Stack
Zvonko Kaiser, Red Hat
The demand for computational power for AI/ML workloads keeps rising. While it is easy to burst out work into the cloud, costs can quickly add up for every spun-up instance. Learn how you can reduce and efficiently manage these computational costs by optimizing the hardware and software stack to fully leverage the features a node in a cluster provides. We'll discuss the latest Kubernetes features for Pod hardware affinity and NUMA awareness, as well as leveraging operators for your AI/ML deployments.