Adaptive Data Evaluation Service with Drift Monitoring
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Solution Overview
Problem
The configuration, training, and evaluation of machine learning evaluators for data sets are complex and time-consuming, with challenges in selecting the most suitable evaluator, managing performance drift, and achieving efficient results, especially when dealing with dynamic data sets and unknown output values.
Innovation Solution
A data evaluation service that selects and trains a set of evaluators capable of achieving specific outcomes over a data set, monitors performance, and replaces evaluators if drift occurs, ensuring continuous and adaptive evaluation results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple evaluators are trained and tested to find the best one, then evaluation accuracy is improved, but development time and resource consumption increase significantly
Solution Approach 1:
The patent applies preliminary action by pre-training multiple evaluators on historical data before they are needed for production. This allows the system to have ready-to-use evaluators that have already undergone extensive training, so when evaluation is needed, pre-trained evaluators can be deployed immediately without waiting for training to complete
Solution Approach 2:
The patent segments the evaluator development process into distinct phases: training phase where multiple evaluators are trained on historical data, evaluation phase where they are tested on validation data, and deployment phase where the best performer is selected. This segmentation allows parallel processing of multiple evaluators during training while maintaining systematic selection criteria
2Reliability
If exhaustive training and testing of evaluators is performed, then evaluator performance is improved, but the process becomes inefficient and protracted
Solution Approach 1:
The patent applies partial action by training evaluators on a subset of historical data (e.g., 70% for training, 30% for validation) rather than exhaustive training on all available data. This partial training approach provides sufficient performance for most applications while dramatically reducing training time and computational resources required
Solution Approach 2:
The patent changes the parameter of training data distribution by using cross-validation techniques where the training/validation split is varied across multiple folds. This allows the system to evaluate evaluator robustness across different data partitions without retraining from scratch, improving reliability efficiency
3Productivity
If a single evaluator is used for data evaluation, then the process is simple and fast, but the system cannot adapt to performance drift or data changes over time
Solution Approach 1:
The patent implements continuity of useful action by continuously monitoring the performance of deployed evaluators against new data streams. This continuous monitoring detects performance drift early, allowing the system to maintain high evaluation speed by switching to pre-trained alternative evaluators before significant degradation occurs
Solution Approach 2:
The patent applies dynamics by creating a dynamic evaluator selection system that adapts to changing data conditions. Multiple evaluators with different training specializations are maintained, and the system dynamically selects the most appropriate evaluator based on current data characteristics, enabling both speed and adaptability
4Adaptability or versatility
If the designer manually configures and trains evaluators, then the evaluation process can be customized, but the complexity and difficulty of the process increase
Solution Approach 1:
The patent applies self-service by implementing automated evaluator selection and deployment systems. The system automatically trains multiple evaluators, evaluates their performance, selects the best performer, and deploys it without requiring manual designer intervention for each step, thereby reducing configuration complexity while maintaining customization through the selection process
Data Source
AI summary
An evaluation platform receives a data set and a description of an outcome, such as predicting results of trends, recognizing patterns, and evaluating options according to specified criteria. The description is evaluated to select candidate evaluators that may be capable of achieving the outcome, and to translate the outcome into a goal for each selected candidate evaluator. The evaluator candidate set is trained using a training data set, and an initial evaluator is selected that exhibits the highest performance to achieve the outcome over the data set. The initial evaluator is applied to achieve the requested outcome over the data set. Optionally, the performance of the initial evaluator may be monitored to detect performance drift. In this event, the evaluator candidate set is reevaluated to identify a substitute evaluator exhibiting higher performance than the initial evaluator, which replaces the initial evaluator in the continued evaluation of the data set.