The Modern ML Monitoring Mess: Categorizing Post-Deployment Issues (2/4)
Read OriginalThis article, part 2 of a series, shifts from a data-centric to a software engineering view of ML monitoring. It categorizes common post-deployment issues in machine learning systems, using the Zillow case to illustrate production instability. The author examines how ML practitioners allocate time and proposes a development process that prioritizes pipeline operationalization and monitoring to improve project success rates.
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