Talks
Autoscaling Generative AI Workloads
at KCD Praha 24
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.
( recording )
KEDA AI/ML KCD kubernetes 2024Multi-Cloud Global Content Distribution at Cloud Native Speeds
at OpenSourceSummit EU 24 @ Vienna
If you’ve been globally distributing digital content for a while, you’ll understand that merely having numerous datacenters with advanced caching patterns isn’t sufficient. When your users need to retrieve an object that’s available in different locations worldwide, they should ideally be directed automatically to the location that’s nearest and fastest for the best experience. Cloud service providers typically offer services to handle this for you within their own clouds, but what if you are running a multi-cloud or hybrid environment? K8GB is a cloud-native solution that handles GeoDNS across heterogeneous environments and enables you to reach the same level of multiregion service resilience offered by cloud providers.
( recording , slides )
k8gb oss kubernetes 2024k8gb meets Cluster API
at FOSDEM 24