Deploying ML workloads with Azure and Kubernetes · The Adventures of Greg

Today we'll be exploring deploying Machine Learning workloads to a Kubernetes cluster deployed on the Microsoft Azure cloud. The main focus of this is not a deep dive on the ML methods used, but to focus instead on the infrastructure considerations when deploying these types of workloads. So it was a good time to try out these technologies used by many ML shops. For a video on how ML companies are using Kubernetes for their workloads check out Building the Infrastructure that Powers the Future of AI . This post will cover the basics of pushing ML jobs onto a Kubernetes cluster.