Open-source version control system for Data Science and Machine Learning projects. Git-like experience to organize your data, models, and experiments.
Bodywork
Bodywork deploys machine learning pipelines to Kubernetes and handles containers and orchestration.
About Bodywork
Bodywork is a command line tool that deploys machine learning pipelines to Kubernetes and handles containers and orchestration. Pipeline stages can run batch jobs, such as preparing features, training models, or computing batch predictions, or run long-running services such as a Flask app serving predictions over HTTP.
Stages are defined as executable Python modules or Jupyter notebooks committed to a project's Git repository and assembled into DAGs through a single bodywork.yaml file. At run time, stages use Bodywork's container image, pull files from Git, install dependencies with pip, and run the designated module or notebook. Use requires a Kubernetes cluster and a Git-based project.
Key features
- Run batch jobs for feature prep, training, batch predictions
- Serve long-running HTTP endpoints such as Flask apps
- Define stages as Python modules or Jupyter notebooks from Git
- Configure pipeline DAGs in single bodywork.yaml file
- Automatic retries and roll-backs for pipeline stages
- Scale service endpoints with multiple container replicas
Best for: Teams that run machine learning pipelines and deploy models to Kubernetes.
Good for
- Avoids building and managing separate container images for pipeline stages
- Uses existing Python modules or notebooks and Git repositories without new APIs
- Supports retries, roll-backs, and replica scaling for service endpoints
Bodywork at a glance
- Type
- Website
- Pricing
- Not confirmed. Check the site
- Platforms
- Web
- Open source
- Not stated
- Language
- English
- Last checked
- Oct 5, 2026 · Online when checked
- Listed
- Oct 5, 2026
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Bodywork FAQ
What types of work can a pipeline stage do?
A stage can run a batch job, such as preparing features, training models, or computing batch predictions, or start a long-running process, such as a Flask app serving predictions via HTTP.
How do I define and configure stages?
Develop an executable Python module or Jupyter notebook, commit it to the project's Git repository, and assemble stages into DAGs in a single bodywork.yaml file.
How are containers handled for each stage?
All stages run using Bodywork's container image, which pulls required files from the Git repository, installs dependencies with pip, then runs the designated module or notebook.
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