Continuously Deploy
Using pre-built Bodywork containers to orchestrate end-to-end machine learning workflows, packaged as Git repositories.
Deploy pipelines developed in Python, to Kubernetes
Using pre-built Bodywork containers to orchestrate end-to-end machine learning workflows, packaged as Git repositories.
From your terminal, you can serve models, schedule batch jobs or deploy complex ML pipelines, using the Bodywork CLI.
All you need to do is provide executable Python modules for starting services and running batch jobs. Bundle these together with a workflow execution plan, into a Git repository and you're ready to go.
Avoid countless hours writing and debugging Kubernetes manifests.
Avoid needing to build and manage your own Docker images.
Automatic retires and roll-backs by default. Services can be backed by multiple container replicas to handle high request volumes.
Built and maintained by machine learning engineers, for machine learning engineers, and committed to remaining 100% open-source.
Learn about the latest features and releases.
