Feature Removal Framework Reducing ML Model Latency
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Solution Overview
Problem
Existing machine learning models face significant resource overhead and scalability issues due to the large number of features used, which increases latency and computational requirements, making it challenging to process requests efficiently in real-time environments.
Innovation Solution
A feature removal framework that calculates importance scores for features and selectively removes features with lower impact, training a simplified version of the model to reduce resource consumption while maintaining performance, by using a threshold-based approach to identify and exclude features with minimal contribution to the model's output.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a large number of features are used in machine learning models, then the accuracy and completeness of analysis are improved, but the resource consumption, latency, and computational overhead increase significantly
Solution Approach 1:
The patent extracts and removes redundant features from the feature set based on importance scoring. The system calculates importance scores for each feature and selectively removes features with scores below a threshold, thereby reducing the feature set size while maintaining the essential information needed for accurate predictions, thus lowering processing latency without significantly compromising analysis accuracy
Solution Approach 2:
The patent changes the parameter of feature dimensionality by dynamically adjusting the number of features based on their importance scores. The system transforms the feature set from a fixed large dimension to a variable reduced dimension, optimizing the balance between analysis accuracy and processing speed by keeping only the most important features
2Measurement precision
If a large number of features are used in machine learning models, then the comprehensiveness of data analysis is improved, but the computational resources and memory requirements increase
Solution Approach 1:
The patent extracts and eliminates computationally expensive redundant features by calculating importance scores and removing features below a threshold. This extraction process reduces the computational burden on processing units and memory systems while retaining the essential features that contribute most to analysis comprehensiveness
Solution Approach 2:
The patent applies partial action by using only a subset of the total available features - specifically, only the top-scoring features above a certain importance threshold. This partial feature set is sufficient to achieve comprehensive data analysis without the excessive computational resources required by the full feature set
3Adaptability or versatility
If the number of features is increased to handle complex data, then the model's analytical capability is improved, but the scalability and ability to meet latency requirements in online environments deteriorate
Solution Approach 1:
The patent extracts and removes features that do not contribute significantly to analytical capability, as determined by importance scoring. This reduction in feature count directly improves request processing throughput and scalability in online environments while maintaining the analytical capability provided by the essential remaining features
Solution Approach 2:
The patent introduces dynamics by making the feature set adaptable - the system can dynamically adjust which features are used based on their importance scores and current performance requirements. This dynamic feature selection enables the model to scale effectively in online environments by optimizing the feature set for both analytical capability and processing speed
Data Source
AI summary
The disclosed embodiments provide a system for streamlining machine learning. During operation, the system determines a resource overhead for a baseline version of a machine learning model that uses a set of features to produce entity rankings and a number of features to be removed to lower the resource overhead to a target resource overhead. Next, the system calculates importance scores for the features, wherein each importance score represents an impact of a corresponding feature on the entity rankings. The system then identifies a first subset of the features to be removed as the number of features with lowest importance scores and trains a simplified version of the machine learning model using a second subset of the features that excludes the first subset of the features. Finally, the system executes the simplified version to produce new entity rankings.