AlexIoannides/bodywork-mlops-demo

Demonstrating how Bodywork can be used to deploy a simulation of the lifecycle of a train-and-serve ML pipeline, responding to new data undergoing concept drift.

Simulating the Lifecycle of a ML Pipeline on Kubernetes

This repository contains a Bodywork machine learning project that simulates the lifecycle of a train-and-deploy pipeline responding to new data undergoing concept drift. Each day a new tranche of synthetic data is simulated and used to test a model deployed as a model-scoring service. The new data is then combined with historical data and used to train a new model that will be used for the following day.