Profile-Based Model Selector for Data Drift Adaptation
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Solution Overview
Problem
Conventional systems fail to dynamically adjust model selection based on data drift, leading to inaccurate results or model failure due to the inability to track compatibility changes between input data and models over time.
Innovation Solution
A profile-based model selector system that compares incoming data profiles to pre-defined criteria using separating hyperplanes in data profile space, adjusts model selection based on performance metrics, and modifies hyperplane boundaries to ensure data compatibility, thereby managing data drift and improving processing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional pre-selectors are used to sort data into models, then model selection is simple and static, but the system cannot adapt to data drift over time leading to model incompatibility
Solution Approach 1:
The patent implements dynamic model selection by continuously monitoring data characteristics and adjusting model assignments in real-time. The system transitions from static pre-selected models to dynamic model routing that adapts to changing data patterns, allowing the system to respond to data drift while maintaining manageable complexity through automated adjustments.
Solution Approach 2:
The system incorporates feedback mechanisms by evaluating model performance metrics and using this information to refine future model selections. Performance feedback loops enable the system to learn from past predictions and adjust to data drift, improving adaptability while the feedback-driven approach keeps complexity manageable through evidence-based adjustments.
2Reliability
If the system dynamically adjusts model selection based on performance metrics, then model reliability improves, but processing time and computational complexity increase
Solution Approach 1:
The system performs preliminary actions by pre-calculating and storing performance metrics, data characteristics, and model compatibility information before actual prediction tasks. This advance preparation enables rapid model selection during inference, maintaining high reliability while minimizing processing time losses through pre-computed decision frameworks.
Solution Approach 2:
The patent applies local quality by optimizing different aspects of the system at different stages: comprehensive performance evaluation is applied locally to specific data-model pairs, while global routing decisions use aggregated performance patterns. This localized optimization approach maintains reliability through detailed assessment while reducing overall processing time through efficient scope management.
3Measurement precision
If the system processes data through multiple models to ensure accuracy, then result accuracy improves, but processing efficiency decreases
Solution Approach 1:
The system segments the model selection process into distinct stages: initial model filtering based on data characteristics, primary model selection using performance metrics, and optional secondary verification for critical predictions. This segmentation enables accurate result production while maintaining processing efficiency by applying comprehensive evaluation only where necessary rather than universally to all data points.
Solution Approach 2:
The patent dynamically changes parameters such as the number of models evaluated, selection thresholds, and verification depth based on data characteristics and confidence levels. For high-confidence, routine predictions, the system uses streamlined single-model processing for efficiency, while switching to multi-model verification only when parameters indicate potential accuracy issues, thus balancing precision and productivity.
Data Source
AI summary
Systems and methods for a profile-based model selector are described. In some aspects, the system receives an input dataset and a corresponding input data profile and determines a similarity metric for the input data profile with respect to each of a plurality of data profiles. Based on the similarity metric for the input data profile being highest with respect to a first data profile, the system processes the input dataset using a first model associated with the first data profile. Based on determining that performance of the first model when applied to the input dataset is above a threshold, the system verifies a separating hyperplane is placed such that the first data profile and the input data profile are included in a first profile domain and a second data profile is included in a second profile domain.