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 2024

Multi-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 2024

k8gb meets Cluster API

at FOSDEM 24

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.

( recording , slides )

k8gb FOSDEM Cluster-API kubernetes 2024