GPU-Accelerated Workloads on KubeVirt - Scaling ML/AI in Kubernetes
CNCF [Cloud Native Computing Foundation] via YouTube
2,000+ Free Courses with Certificates: Coding, AI, SQL, and More
AI Engineer - Learn how to integrate AI into software applications
Overview
Google, IBM & Meta Certificates — All 10,000+ Courses at 40% Off
One annual plan covers every course and certificate on Coursera. 40% off for a limited time.
Get Full Access
Explore how to enable GPU-accelerated virtual machines in KubeVirt for demanding AI/ML workloads in this lightning talk from the Cloud Native Computing Foundation. Learn the technical foundations for running GPU-backed VMs in Kubernetes environments and discover why this approach is gaining popularity for secure, scalable, and isolated inference pipelines. Examine the key differences between container-based and VM-based GPU allocation strategies, and understand how KubeVirt seamlessly integrates with CNCF tools like Prometheus and the Kubernetes scheduler to monitor and optimize performance. Gain practical insights into pushing KubeVirt beyond typical virtual machine use cases into production-ready machine learning and artificial intelligence workloads, with technical guidance on implementation and real-world applications for scaling ML/AI infrastructure in cloud-native environments.
Syllabus
GPU‑Accelerated Workloads on KubeVirt: Scaling ML/AI in Kuberne... Amandeep Singh and Shivani Tiwari
Taught by
CNCF [Cloud Native Computing Foundation]