Follow the steps below to begin deploying pipelines, or browse our library of templates and tutorials
1.
Bodywork is distributed as a Python package. Download it from PyPI.
2.
Add a bodywork.yaml file to your Python project’s Git repo. The contents of this file describes how your project will be deployed.
3.
Bodywork is used from the command-line to deploy pipelines to Kubernetes clusters. With a single command, you can start Bodywork containers, hosted by us on Docker Hub, that will pull Python modules directly from your Git repo, and run them.
That's it, you've now deployed your pipeline to Kubernetes!
You don’t need to build your own Docker images or understand how to create Kuberentes resources.
Bodywork will fill the gap between executable Python modules and operational jobs and services.
If you’re new to Kubernetes, then check out our guide to Kubernetes for ML - we’ll have you up-and-running with a test cluster on your local machine, in under 10 minutes.
Use one of the templates below to get a head start deploying your project
Deploying a model scoring-service.
Batch-scoring a dataset with a pre-trained ML model.
How to deploy an automated train-and-serve pipeline.
A guide to deploying MLflow (and other Python services) to Kubernetes, using Bodywork.
Build ML pipelines using Jupyter notebooks with Bodywork.
Rapid deployment of Scikit-Learn models to Kubernetes, using FastAPI with Bodywork.
Deploying probabilistic programs using PyMC3 with Bodywork.
Train a ML model and then deploy a Plotly dashboard to visualise performance metrics.
More templates and tutorials can be found on GitHub
Learn about the latest features and releases.
