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Training-serving skew: Difference between revisions
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(Created page with "{{see also|Machine learning terms}} ===Training-Serving Skew in Machine Learning== Training-serving skew is a common issue when deploying machine learning models, particularly in production settings where they will be put to real world use. This term describes the difference in performance of a model during training and deployment that can arise from various sources such as different data distributions, hardware configurations or software dependencies between these envir...") |
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{{see also|Machine learning terms}} | {{see also|Machine learning terms}} | ||
===Training- | ==Introduction== | ||
Training-serving skew is a common issue when deploying machine learning models, particularly in production settings where they will be put to real world use. This term describes the difference in performance of a model during training and deployment that can arise from various sources such as different data | [[Training-serving skew]] is the difference between a [[model]]'s performance during [[training]] and that same model's performance during [[serving]] ([[inference]]). Training-serving skew is a common issue when [[deploying]] [[machine learning models]], particularly in production settings where they will be put to real world use. This term describes the difference in performance of a model during training and [[deployment]] that can arise from various sources such as different [[data distribution]]s, hardware configurations or software dependencies between these environments. | ||
==Sources of Training-Serving Skew== | ==Sources of Training-Serving Skew== | ||