What Is MLOps?
MLOps is the set of practices that combine machine learning, DevOps, and data engineering to automate and standardise the deployment, monitoring, and lifecycle management of machine learning models in production.
MLOps — the set of practices that combine machine learning, DevOps, and data engineering to automate and standardise the deployment, monitoring, and lifecycle management of machine learning models in production.
MLOps is the operational infrastructure underlying AI governance. Model registries, deployment pipelines, automated retraining, and monitoring systems are MLOps concerns. From a governance perspective, MLOps is the mechanism through which controls are implemented consistently at scale: model version control, automated bias testing in the deployment pipeline, A/B testing guardrails, and drift detection alerts. Without MLOps discipline, AI governance policies exist as documents but are not enforced in practice.
Source: NIST AI RMF, MANAGE 2.2; ISO/IEC 42001:2023, Clause 8
Plain-language explanation
MLOps is the operational infrastructure underlying AI governance. Model registries, deployment pipelines, automated retraining, and monitoring systems are MLOps concerns. From a governance perspective, MLOps is the mechanism through which controls are implemented consistently at scale: model version control, automated bias testing in the deployment pipeline, A/B testing guardrails, and drift detection alerts. Without MLOps discipline, AI governance policies exist as documents but are not enforced in practice.
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