From ML Models to Intelligent Applications: The Rise of MLOps
Summary: Shifts from model-centric ML to end-to-end ML-driven applications, framing MLOps as a data-management and deployment problem. Outlines ML-specific abstractions for testing, monitoring, retraining, and data/feature management, plus collaboration and fairness/explainability as core open challenges. (summarized by gpt-5-nano on Feb 09 2026)
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