What is Machine Learning Model Drift

Kanth Mentorship Show

Many machine learning models tend to be black boxes, where explainability is very limited, which can make it difficult to understand why a model is not performing as expected. This is especially true with regard to how a model performs over time with new training data.
The machine learning lifecycle begins with data warehousing, ETL pipelining, and model training. The next stages in the lifecycle: deployment, management, and operations. Machine learning deployment plays a critical part in ensuring a model performs well, both now and in the future, but it is also vitally important to understand model monitoring and model drift to that same end
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