Bodywork is flexible enough to deploy almost any type of Python project to Kubernetes. We demonstrate this by using it to deploy a production-ready instance of MLflow, then show how MLflow can be used alongside Bodywork's ML deployment capabilities, to form a powerful open-source MLOps stack.

Get up-and-running with Kubernetes in under 10 minutes from a standing-start.

On the benefits of training the simplest model conceivable and deploying it to production, as soon as you can. We show how to make this Agile approach to developing machine learning systems a reality, by demonstrating that it takes under 15 minutes to deploy a Scikit-Learn model, using FastAPI with Bodywork.

bodywork-core v2.0.0 is available to download from PyPI.

Bodywork, GitOps and machine learning deployment - Alex Ioannides presents at DataTalks.Club.

bodywork-core v1.0.0 is available to download from PyPI.