Data Science Model Maturity Scoring System
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Solution Overview
Problem
Managing and maintaining hundreds of complex data science models across various lines of business is challenging due to the diversity of models, platforms, and sharing methods, requiring significant resources and scaling difficulties, especially when ensuring models remain current and performing as intended.
Innovation Solution
A system and method that utilize a data science model score database and server to automatically calculate and adjust maturity scores based on scalable, service-oriented, and productized scores, allowing for centralized management and monitoring of data science models, enabling efficient resource allocation and model updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional manual monitoring and maintenance solutions are used for data science models, then model performance can be ensured, but resource requirements increase and scalability is limited
Solution Approach 1:
The system enables self-service through automated self-assessment mechanisms where models automatically evaluate their own performance metrics and data quality without requiring manual intervention from data scientists, thereby maintaining reliability while eliminating the need for additional human resources
Solution Approach 2:
Manual monitoring and maintenance processes are replaced with automated computational systems that continuously track model performance, assess data quality, and generate maintenance alerts, substituting human mechanical work with automated digital processes that scale without additional resources
2Reliability
If additional teams and resources are deployed to perform model validation and health checks, then model currency can be maintained, but resource burden increases
Solution Approach 1:
The automated system performs multiple functions including performance monitoring, data quality assessment, drift detection, and maintenance scheduling within a single unified platform, eliminating the need for separate teams for each function and reducing overall resource requirements while maintaining model currency
Solution Approach 2:
The system implements continuous automated monitoring and assessment of model performance and data quality, replacing periodic manual checks with uninterrupted automated surveillance that maintains model currency without requiring additional human resources at any point in time
3Loss of information
If comprehensive monitoring of hundreds of complex data science models is implemented, then model performance can be tracked, but system complexity increases
Solution Approach 1:
The system segments the monitoring process into distinct automated modules: data quality assessment, performance metric tracking, drift detection, and maintenance alerting. Each module handles specific aspects independently, reducing overall system complexity while maintaining comprehensive monitoring of hundreds of models
Data Source
AI summary
A data science model score database may contain electronic records, each including a data science model identifier and a set of data science model scores. A data science model score server, coupled to the data science model score database, may receive (from a remote user device) an indication of a selected data science model. The server may then retrieve, from the data science score database, information about the selected data science model. Based on the retrieved information, the server may automatically calculate a maturity score for the selected data science model in accordance with a scalable score, a service-oriented score, a validated score, and a productized score. When the server receives from the remote user device an adjustment to at least one of the scalable score, the service-oriented score, the validated score, and the productized score, it may automatically re-calculate the maturity score for the selected data science model.