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.
This repository was archived by the owner on Aug 7, 2023. It is now read-only.
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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.
AlexIoannides/bodywork-mlops-demo
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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.