Imagine teaching a machine learning model to predict customer behavior, but there’s a catch.

You have two vastly different groups: millions of regular buyers and just a few thousand resellers, each with distinct behaviors.

The traditional one-model-fits-all approach often falls short, especially when dealing with diverse data patterns and rare but significant cases.

How can we ensure our model captures the nuances of rare yet critical subgroups without sacrificing overall accuracy?

Welcome to the cascade design pattern in ML/AI, an approach that systematically decomposes complex problems into smaller, more manageable subproblems.

Let’s explore how this pattern works, why it’s effective, and how you can leverage it to tackle real-world challenges.



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