Kubernetes for Data Scientists

Data scientists and machine learning engineers shouldn’t necessarily need to know too much about Kubernetes (K8s), yet many are interested and it can be a differentiator in the job market. So we’re here to help! This livestream will be a gentle introduction to K8s for data scientists and MLEs who want to take their production DS/ML chops to the next level.

Bryan Galvin, who works on ML infrastructure at Best Egg, and Shri Javadekar, A K8s insider and engineer at Outerbounds, join Hugo Bowne-Anderson to introduce K8s to those who want to know a bit more. In this livestream, you’ll learn

– What K8s is and what it isn’t;
– Why you should care;
– What a typical journey in moving from statistics, data analysis and data science to infrastructure looks like (this is Bryan’s journey!);
– The differences between K8s, Docker, and other moving parts (such as Terraform);
– How to understand the lifecycle of a K8s project;
– What the core concepts of deploying K8s workloads are;

We’ll also do some live coding and show you how to take generative AI code that you’ve prototyped locally and that you’ve prototyped locally and

– Deploy it to a K8s cluster;
– Scale the workload, and
– Schedule it!

And much more! The fireside chat will be followed by an AMA with Bryan, Shri, and Hugo at slack.outerbounds.co.