A Case for Serverless Machine Learning

The scale and complexity of ML workflows makes it hard to provision and manage resources—a burden for ML practitioners that hinders both their productivity and effectiveness. Encouragingly, however, serverless computing has recently emerged as a compelling solution to address the general problem of data center resource management. This work analyzes the resource management problem in the specific context of ML workloads and explores a research direction that leverages serverless infrastructures to automate the management of resources for ML workflows. We make a case for a serverless machine learning framework, specializing both for serverless infrastructures and Machine Learning workflows, and argue that either of those in isolation is insufficient.