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Concept for reliably organizing and operating AI/ML systems with a focus on monitoring, deployment and governance.
AI Operations defines organizational, process and technical practices for reliably operating AI/ML systems. It combines monitoring, continuous delivery, model governance and infrastructure automation to ensure performance, reliability and compliance. It addresses technical metrics and organizational feedback loops for continuous improvement.
Share of inputs where distribution has significantly shifted compared to the training baseline.
95th percentile of response times for production inference requests.
Average time to restore normal model functionality after an outage.
Use of ML models for anomaly detection in infrastructure metrics and automated incident responses.
Pipeline automates data validation, model training, testing and production rollout including rollback strategies.
Rule‑based checks, explainability reports and audit trails to comply with regulatory requirements.
Take stock of models, data flows and existing tools
Define central metrics, SLAs and alerting rules
Introduce versioned pipelines and automated tests
Build an observability layer for models and features
Establish governance processes and review boards