Multi-Stage Data Processing Filtering for ML Inference
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Large volumes of data from machine learning models require significant computational resources for processing, and existing methods often inefficiently process all prediction data, leading to a computing burden that can be compounded by downstream models, especially when dealing with confidential and restricted data.
Innovation Solution
A system and method for optimized multi-stage processing that identifies and filters records meeting predetermined thresholds, generating filtered inference data with threshold columns, which reduces the computational load by concentrating processing on relevant records, and allows for efficient downstream processing without unnecessarily reducing the output of the first model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all prediction data from machine learning models is processed by downstream models, then comprehensive analysis is achieved, but computational resources are excessively consumed
Solution Approach 1:
The patent extracts and processes only the most relevant features and predictions from machine learning models before passing them to downstream models. This selective extraction reduces the volume of data requiring intensive computational processing while retaining the essential information needed for comprehensive analysis.
Solution Approach 2:
The patent applies different processing strategies to different portions of the data based on their importance and characteristics. High-priority predictions receive full downstream processing, while lower-priority data receives simplified processing or is filtered out, optimizing computational resource allocation across the data spectrum.
2Productivity
If filtered inference data with threshold columns is generated, then downstream processing is simplified and accelerated, but additional processing steps are required
Solution Approach 1:
The patent performs filtering and threshold evaluation in advance, before data reaches downstream models. By pre-computing which records meet predetermined thresholds and organizing them accordingly, the system eliminates the need for downstream models to perform redundant filtering operations, thereby simplifying their processing pipeline.
Solution Approach 2:
The patent introduces an intermediate processing layer that sits between the machine learning models and downstream models. This intermediary component handles the filtering, threshold evaluation, and data organization, acting as a mediator that prepares data in an optimized format for downstream consumption without requiring downstream models to perform complex filtering logic.
3Productivity
If computational resources are conserved by filtering data, then processing efficiency improves, but potential relevant data may be lost
Solution Approach 1:
The patent dynamically adjusts filtering parameters and thresholds based on data characteristics, model performance metrics, and business requirements. By optimizing these parameters, the system achieves effective filtering that reduces computational load while maintaining sensitivity to relevant patterns and minimizing false negatives.
Solution Approach 2:
The patent implements feedback mechanisms where downstream model performance and data characteristics inform adjustments to filtering parameters. This closed-loop approach ensures that filtering thresholds are continuously optimized to balance computational efficiency with the retention of relevant information, preventing both over-filtering and under-filtering.
Data Source
AI summary
Systems and methods for optimizing multi-stage data machine learning model processing, by providing an intermediate processing step to identify or filter records in voluminous inference data that meet a predetermined threshold. Multiple downstream processes can be optimized by applying multiple thresholds to the voluminous inference data.