HybridOps AI/ML Pipeline for SaMD Drift Monitoring and Compliance
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Solution Overview
Problem
The gap between AI/ML industry standards and regulatory practices for medical devices (SaMD) leads to slow software development, with existing CI/CD pipelines not being permitted, and there is a need for efficient training, validation, and deployment of AI/ML systems that adhere to regulatory requirements.
Innovation Solution
A HybridOps system integrates DevOps and ML-Ops for AI/ML based SaMD systems, deploying a public-facing model with continuous monitoring and two non-public clones for development and reference, enabling continuous improvement through feedback loops and model drift monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional CI/CD pipelines are used for AI/ML model deployment, then development speed and continuous improvement are enhanced, but regulatory compliance for medical devices cannot be ensured
Solution Approach 1:
The system segments the deployment pipeline into three distinct clones: a public-facing approved model for regulatory compliance, a development clone for continuous improvement using ML-Ops, and a standard reference clone for drift detection. This segmentation allows each component to serve its specific purpose without compromising overall compliance.
Solution Approach 2:
The patent creates copies (clones) of the approved AI/ML model. The development clone replicates the approved model's structure and functionality, enabling continuous training and improvement. The standard reference clone serves as a static reference point for detecting model drift, ensuring that improvements don't compromise regulatory compliance.
2Adaptability or versatility
If continuous development and model updates are implemented, then model performance and adaptability improve, but maintaining approved indications and scope of operation becomes difficult
Solution Approach 1:
The system implements feedback loops where the development clone receives performance data and user feedback, processes improvements through ML-Ops pipelines, and undergoes validation before updates can be applied to the public-facing approved model. This ensures continuous improvement while maintaining regulatory boundaries.
Solution Approach 2:
The patent monitors and controls parameter changes between the development clone and standard reference clone to detect model drift. By tracking parameter variations and comparing them against predefined thresholds, the system ensures that model updates remain within approved indications and scope of operation.
3Reliability
If three clone deployments are implemented for compliance and development, then regulatory compliance and continuous improvement are both achieved, but system complexity increases
Solution Approach 1:
The HybridOps platform provides a universal framework that manages all three clones through standardized interfaces and workflows. The system handles model deployment, monitoring, validation, and updates across all clones using unified processes, reducing the operational complexity despite the multi-clone architecture.
Data Source
AI summary
Systems and methods provide a HybridOps model for the identification, capture, isolation, feature engineering and adjudication of source signal data signatures for inclusion in calibration quality standard reference signal data signature libraries that improve machine learning and validation, reduces model bias and reduces model drift. The HybridOps model may include an “unlocked” AI/ML (machine learning enabled) public facing deployment pipeline in parallel with a clone AI/ML deployed in an internal development environment using a ML-Ops pipeline and in parallel with a clone“locked” AI/ML (machine learning disabled) as a standard reference. The three deployed models enables monitoring and measuring model drift, context drift and product progression for improved verification and validation of model reliability. The parallel environment AI/ML testing using randomized learning, validation, testing sets drawn from calibration quality adjudicated standard reference signal data signature libraries provides for validation, verification and test to failure procedures.