Model Drift in Machine Learning – How To Handle It In Big Data - KDnuggets

The Rendezvous architecture proposed by Ted Dunning and Ellen Friedman in their book on Machine Learning Logistics was a wonderful solution I found for a specific architectural problem I was working on. I was looking for a tried and tested design pattern or architectural pattern that helps me run Challenger and Champion models together in a maintainable and supportable way. The rendezvous architecture was significantly more useful in the big data world because you are dealing with heavy data and large pipelines. The ability to run Challenger and Champion models together on all data is a very genuine need in machine learning, where the model performance can drift over time and where you want to keep improving on the performance of your models to something better always. So, before I delve deeper into this architecture, I would like to clarify some of the jargon I have used above.