ML Model Registry for Continuous Feedback and Adaptation
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Solution Overview
Problem
Organizations face challenges in deploying and continuously improving machine learning models due to fragmentation and the lack of a framework to capture and integrate expert knowledge, leading to limited learning and ineffective decision-making.
Innovation Solution
A method that allows users to provide feedback to machine learning models, either at the model level or training dataset level, which is incorporated to improve the model, enabling continuous learning and adaptation to real-world conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If machine learning models are deployed as point-solutions or API endpoints, then specific business needs are addressed, but fragmentation occurs and limited learning is achieved
Solution Approach 1:
The patent combines multiple isolated machine learning models into a unified model registry that serves as a centralized repository. This registry enables models to be shared, versioned, and reused across different use cases, transforming fragmented point-solutions into an integrated system that promotes organizational learning and adaptability.
Solution Approach 2:
The model registry creates a universal platform that serves multiple functions: storing models, versioning them, facilitating sharing across teams, enabling reuse in different contexts, and supporting continuous learning. This multi-functional system allows a single infrastructure to support diverse machine learning initiatives throughout the organization.
2Loss of information
If expert knowledge is captured in centralized repositories, then knowledge sharing is improved, but the complexity of integrating and maintaining the framework increases
Solution Approach 1:
The model registry enables users to independently upload, version, and share their machine learning models without requiring complex manual integration processes. The system automatically manages versioning, metadata storage, and accessibility, allowing expert knowledge to be captured and preserved through self-service mechanisms that reduce the burden of framework maintenance.
3Reliability
If feedback loops are implemented for continuous model improvement, then model performance is enhanced, but the time and resources required for monitoring and re-training increase
Solution Approach 1:
The model registry establishes continuous feedback loops where model performance is continuously monitored in production environments. This ongoing process automatically captures performance metrics, identifies degradation, and triggers re-training cycles, ensuring models maintain high performance without requiring manual intervention at each stage of the improvement cycle.
Solution Approach 2:
The system implements automated feedback mechanisms that continuously monitor model performance in production and feed this information back into the model registry. This feedback drives automatic re-training and version updates, creating a self-improving system that enhances model reliability while minimizing manual maintenance time through automation.
Data Source
AI summary
Disclosed herein is a method of providing feedback to a machine learning model. The method includes allowing a user to observe an output of a trained machine learning model; allowing the user to input feedback to the machine learning model based on the output, wherein the feedback is on at least one of a model level or on a training dataset level; and incorporating the feedback into the machine learning model to improve the machine learning model, wherein the method is performed using one or more processors. Disclosed herein are one or more computer-readable storage media including computer executable instructions which when executed by the one or more processors cause the one or more processors to perform the method. Disclosed herein is a computer system which includes one or more processors and one or more computer-readable storage media which include computer executable instructions which when executed by the one or more processors cause the one or more processors to perform the method.