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    Software & AIAI / ML in DevicesSoftware LifecycleMLOps

    MLOps for Medical Devices

    The set of practices and tooling for deploying, monitoring, retraining, and governing machine learning models in regulated medical device software environments.

    Reviewed by Christian Espinosa, Founder, Blue Goat CyberLast reviewed June 20, 2026

    Definition

    MLOps for medical devices is the discipline of operating AI/ML models in production under medical device quality system controls. It extends generic MLOps (model versioning, CI/CD pipelines, monitoring, feature stores, experiment tracking) with the additional requirements of IEC 62304 (software lifecycle), ISO 13485 (QMS), 21 CFR Part 820/QMSR (design controls), AAMI CR34971 (AI risk management), and the change-control discipline of a PCCP/ACP. Core MLOps capabilities for medical devices include validated training pipelines, immutable model registries, drift monitoring with patient-safety thresholds, controlled rollback, and audit trails tying every production prediction back to a versioned model and its qualification evidence.
    What the regulation says
    FDA's PCCP guidance (2024) implicitly requires MLOps practices to execute an Algorithm Change Protocol. IEC 62304 §5 (development) and §6 (maintenance) apply equally to ML components. AAMI CR34971 is the consensus reference for AI/ML risk management feeding MLOps monitoring.

    What this means in practice

    FDA-cleared AI/ML devices that include a PCCP rely on MLOps infrastructure to make iterative updates safe and traceable. The most common gap is connecting MLOps tooling to the quality system, many ML teams have excellent versioning and monitoring but cannot demonstrate the traceability and document control regulators expect. Mature MLOps for medical devices unifies the data science workflow with the design history file rather than maintaining them as parallel universes.
    Common pitfalls
    • •Maintaining ML pipelines outside the QMS, making production changes without design control evidence creates inspection findings.
    • •Monitoring model performance with engineering metrics only (accuracy drift) without patient-safety thresholds, drift alone isn't actionable.
    • •Treating MLOps as a generalist DevOps task, the data lineage, model lineage, and dataset versioning requirements are materially stricter than for non-ML software.

    Primary references

    3 sources
    Link health: 3 bot-blocked· last checked 2026-06-20
    1. 1
      PCCP Guidance for ML-Enabled Devices
      Bot-blocked
      FDAfda.gov
    2. 2
      FDA - Software as a Medical Device (SaMD)
      Bot-blocked
      FDAfda.gov
    3. 3
      FDA - AI/ML-Enabled Medical Devices
      Bot-blocked
      FDAfda.gov

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