MLOps — Machine Learning Operations and AI Governance Infrastructure

Definición

Definición técnica

MLOps (Machine Learning Operations) is the set of practices, tools, and cultural principles that streamline and automate the processes of deploying, monitoring, managing, and continuously improving machine learning models in production environments. It adapts DevOps principles — automation, continuous integration and delivery, monitoring, collaboration — to the specific challenges of ML systems: data versioning, experiment tracking, model versioning, pipeline automation, and continuous monitoring of model performance.

From a governance perspective, MLOps is the engineering discipline that implements model lifecycle management in production. Well-designed MLOps practices produce the artifacts — data lineage records, training run logs, model version registries, performance monitoring dashboards, drift detection alerts — that AI governance frameworks require. Poorly designed MLOps creates the documentation gaps, traceability failures, and monitoring absences that become governance vulnerabilities.

ISO/IEC 42001 Annex A includes controls covering the technical pipeline management that MLOps implements. The EU AI Act’s technical documentation, logging, and post-market monitoring requirements are operationalized through MLOps infrastructure.

Por qué importa operativamente

MLOps matters for governance because it is the layer where governance intentions either become operational reality or remain aspirational. An organization can have an excellent AI governance policy and ISO 42001 certification while simultaneously operating AI systems with no version control, no drift detection, no automated monitoring, and no audit trail for individual decisions. The MLOps infrastructure — or its absence — is where the gap between governance design and governance practice becomes visible.

For high-risk AI systems under the EU AI Act, MLOps is the technical infrastructure that must produce the compliance artifacts: logging systems for Article 12, monitoring systems for Article 72, model documentation for Annex IV, and change management records for post-market monitoring obligations. Organizations that lack mature MLOps infrastructure face a technical debt problem before they face a governance problem — and the two are directly linked.

Marco regulatorio / Regulatory Framework

| Framework | MLOps relevance | | — | — | | EU AI Act — Arts. 12, 72 | Automatic logging requirements (Art. 12) and post-market monitoring (Art. 72) are directly implemented by MLOps infrastructure. | | EU AI Act — Annex IV | Technical documentation on training data, model architecture, and test results is produced by MLOps processes. | | ISO/IEC 42001 | Annex A controls on model lifecycle, monitoring, and change management are implemented in practice through MLOps. | | NIST AI RMF | The NIST AI RMF Measure and Manage functions depend on the monitoring and traceability infrastructure that MLOps provides. |

Cómo lo evalúa Zertia

Zertia evaluates MLOps maturity as part of the AI Model Audit and ISO/IEC 42001 certification. The assessment examines whether the MLOps infrastructure produces the governance artifacts required by the EU AI Act and ISO 42001: model version registries with training provenance, automated performance monitoring with defined alert thresholds, logging systems that capture decision-level traceability, and change management processes that document model updates and trigger revalidation when required.

→ [AI Model Audit] · ISO 42001 Certification — [zertia.ai/services]

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EU AI Act compliance requires logging, monitoring, and documentation that MLOps infrastructure must produce. Zertia evaluates whether your MLOps practices meet the technical requirements of the regulation.

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Por qué importa operativamente

MLOps importa para la gobernanza porque es la capa donde las intenciones de gobernanza se convierten en realidad operativa o permanecen aspiracionales. Para sistemas de alto riesgo bajo el EU AI Act, MLOps es la infraestructura técnica que debe producir los artefactos de cumplimiento.

Marco regulatorio

Framework	Relevancia de MLOps
EU AI Act — Arts. 12, 72	Los requisitos de registro automático (Art. 12) y monitorización post-mercado (Art. 72) son implementados directamente por la infraestructura MLOps.
EU AI Act — Annex IV	La documentación técnica sobre datos de entrenamiento, arquitectura del modelo y resultados de pruebas es producida por los procesos MLOps.
ISO/IEC 42001	Los controles del Anexo A sobre ciclo de vida del modelo, monitorización y gestión de cambios son implementados en la práctica a través de MLOps.
NIST AI RMF	Las funciones Measure y Manage del NIST AI RMF dependen de la infraestructura de monitorización y trazabilidad que MLOps proporciona.

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Cómo lo evalúa Zertia

Zertia evaluates MLOps maturity as part of the AI Model Audit and ISO/IEC 42001 certification. The assessment examines whether the MLOps infrastructure produces the governance artifacts required by the EU AI Act and ISO 42001: model version registries with training provenance, automated performance monitoring with defined alert thresholds, logging systems that capture decision-level traceability, and change management processes that document model updates and trigger revalidation when required.

[AI Model Audit] · ISO 42001 Certification