How Feature Stores Solve the Training-Serving Skew Problem
Training-serving skew is one of those failures that rarely shows up in a unit test. A model may perform exceptionally well during offline evaluation but degrade after deployment because features are calculated differently at inference time. Understanding feature consistency, data pipelines, and real-world ML deployment is an important part of a Data Science Course in Chennai at FITA...
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