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Data Drift

Data drift is unexpected and undocumented changes to data structure, semantics, and infrastructure that is a result of modern data architectures. Data drift breaks processes and corrupts data, but can also reveal new opportunities for data use. In simple terms, the distribution of the data changes.

Imagine your ML model tracks the kind of mobile device your user uses. Over time, new phones will come in and old ones will go away. Thus your distribution will change.

Data Drift can be a huge problem in your Machine Learning Pipelines. Remember that AI agents work by learning the probability distribution of your target data wrt to the features. Drift will mess this up.

Both detecting and dealing with data drift are very important aspects of Machine Learning. Remember the adage, “Garbage in, Garbage Out.” Data drift can turn your features into Garbage.

Handling this would require a thorough understanding of statistical distributions, model learning behavior, and data pre-processing. Like the video (and follow) to learn more about these topics. Ways to support my content are in the YT video description.


This is part of my series Machine Learning Terms. Here I share various #ML and AI terms in less than a minute to help you learn about more ideas and concepts. With these concise videos, you will know the basics of diverse machine learning concepts.


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