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.
These days k8s namespaces don't provide enough isolation for our cloud native experiments. It's much easier to give a user the whole cluster to play with. Let them to break it; repeat. However, this assumes the cluster creation and deletion is an easy thing to do. Also there should be a nice API for that, not just some 5 years old web. Have you ever heard about clusterctl? If not, then come to this talk to learn how easy it is to start using it. If yes, then come to this talk to learn how hard it is to use it in production. Cluster API (CAPI) is a unique standardization effort among multiple cloud providers such as GCP, AWS, Azure but can also work with on-prem solutions such as OpenStack, KVM or vSphere. It allows you to dedicate one cluster in your infra as a control plane for creating the workload clusters. If you are into self-replicating robots, you are going to love this API!