Kaslin Fields

Recently, I’ve been travelling around the country to help engineers learn how to build MCP Servers and AI Agents serverlessly on Cloud Run in our Accelerate AI with Cloud Run workshops. Attendees often ask, how can I use what I learned in my use case?

This post finally continues my AI exploration series with a look at the open source solution Ray and how it can support AI workloads on Google Kubernetes Engine. What is Ray? Initially created at UC Berkeley in 2018, Ray is an open-source unified compute framework to scale AI and Python workloads. The Ray project is predominantly managed and maintained by Anyscale. At a high level, Ray is made up of 3 layers: Ray AI Libraries (Python), Ray Core, and Ray Clusters.

Just like so many in the tech industry, Artificial Intelligence (AI) has come to the forefront in my day-to-day work. I've been starting to learn about how "AI" fits into the world of Kubernetes - and vice versa. This post will start a series where I explore what I'm learning about AI and Kubernetes. To describe the AI workloads engineers are running on Kubernetes, we need some terminology. In this post I’m going to describe two major types of workloads: training and inference.