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Building and Maintaining ML Systems

Building and Maintaining ML Systems

E仙人
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Machine learning projects typically consist of a single monolithic model trained on a large labeled data set.  If the model’s summary performance metrics (e.g., accuracy, F1 score) were the only requirements and the performance remained unchanged, adding examples would not be a problem even if the new model errs on the examples that were previously predicted correctly.  However, for many problems for which predictability and quality control are important, any negative progress on the model quality leads to laborious testing of the entire model and incurs high maintenance cost.  A single monolithic model lacks the modularity required for most people to isolate and address the root cause of a regression problem. 
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