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Data, business needs and models change after launch, requiring defined monitoring, updates and rollback.
Continuously evaluate business and model changes, monitor drift and operating costs, and maintain knowledge, rules, routing and specialized model versions.
Data, business needs and models change after launch, requiring defined monitoring, updates and rollback.
Establish quality and cost baselines; monitor drift and errors; collect feedback and difficult cases; evaluate, approve and release through quality gates.
Operating reports, cost and drift monitoring, update plans, regression evaluation, version records and rollback plans.
Describe input types, correct outcomes, the current system, main problem, deployment constraints and expected deliverables. Sample transfer follows confirmation of authorization and scope.
Input types, current outcomes and metrics to improve.
Cloud, private environment or edge devices, with latency, compute and budget constraints.
Agree on base-model licensing and rights to customer-specific outputs and general methods.
Quality, stability, latency, cost per valid result and deployment fit determine the solution. Evaluation and training data are managed separately; expert review and regression gates control changes.