What is the Definition of Done for AI/ML Model Deployment?
The DoD for AI/ML models extends traditional software DoD to include criteria specific to model performance, data drift, ethical compliance, explainability, and robust monitoring in production environments.
Deploying AI/ML models introduces unique challenges that necessitate a specialized Definition of Done. Beyond typical software engineering criteria like code quality and successful integration tests, an AI/ML DoD must ensure the model meets predefined performance metrics (e.g., accuracy, precision, recall) on unseen data, passes fairness checks, and provides sufficient explainability for its predictions.
Critical components include successful deployment to a production environment, establishment of robust monitoring for data drift and model decay, validation of ethical guidelines, and ensuring explainability mechanisms are in place. Furthermore, the DoD must verify that rollback procedures are clearly defined and tested, and that the model's impact on business objectives can be continuously measured post-deployment, aligning with MLOps best practices.
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