Short lightning talk about KEDA being used as autoscaler for AI/ML workload. Stable diffusion model was used as an example that generates images based on the text input. Demo application was scaling the worker pods based on the length of message queue. I also briefly talks about pitfalls of GPU intensive workloads on K8s.
In this talk we will be talking about an open-source way to fully automated K8s clusters that can host workloads that can survive any failure, using pure DNS as the underlying tool for switching the communication among available Kubernetes clusters. No single vendor lock-in. Workloads can be deployed in AWS, Azure, GCP, on-prem. The only common denominators are Kubernetes and Cluster-API.