Mature organizations run ML workloads on Kubernetes, but implementations vary widely, and ML engineers rarely enjoy the streamlined development and deployment experiences that platform engineering teams provide for software engineers. Making small changes takes an hour to test and moving from research to production frequently takes multiple weeks – these unergonomic and inefficient processes are unthinkable for software, but standard in ML. Kubetorch is an introduction of a novel compute platform that is Kubernetes-native that offers a great, iterable, and debuggable interface into powerful compute for developers, without introducing new pitfalls of brittle infrastructure or long deployment times.
