Contributions

Tutorial
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.
Tutorial
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.
Library
Have an idea for a Python package? Register the name on PyPI ๐Ÿ’ก
Tutorial
A tutorial on how to automate the deployment of machine learning pipelines, using the Bodywork MLOps framework, Git and Kubernetes.
Library
MLOps tool for deploying machine learning projects to Kubernetes ๐Ÿš€
Tutorial
A tutorial on how best to structure ETL jobs written for PySpark, so that they are robust, testable and ready for production.
Tutorial
Demonstrating the benefits of using Bayesian Inference and PYMC3 for estimating the parameters of stochastic process commonly used in quantitative finance.
Tutorial
An introduction to Python machine learning model deployment operations (MLOps) using Flask, Docker, Kubernetes and Seldon-Core.
Article
A quick introduction and comparison of two Bayesian inference algorithms applied to a common linear regression task, using PYMC3.