PRODUCTION SCENARIO
A retailer's data science team has trained 14 versions of a demand forecasting model over two years. Nobody can say which version serves production traffic or how its evaluation compared with the previous one.
Which stage of the machine learning lifecycle is missing, and which capability addresses it?
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Answer: Model management, using Model Registry to track versions and deployments
Once training is finished the work moves to model management, and Model Registry is the central repository for it: tracking model versions, evaluating quality, and deploying models for serving. Preparation and training tools do nothing about version tracking when the models already exist.