Deploying new AI models into production requires a structured rollout plan to mitigate risks such as changes in answer quality, latency, and cost. A phased approach, including local smoke tests, staging evaluations, shadow testing, and canary releases, is recommended. Each stage should have defined success criteria and rollback triggers, such as increased error rates or latency, to ensure a safe transition and maintain system stability. AI
IMPACT Provides a framework for AI teams to manage model updates, ensuring stability and performance in production environments.
RANK_REASON The item provides practical guidance on deploying existing AI models, not a new release or research.
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