Machine Learning Pipeline Post-Processing for Device-Specific Detection
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Solution Overview
Problem
Configuring machine learning models for specific applications and devices is time-consuming and burdensome due to varying device complexities, performance requirements, and constraints, as well as the need for optimizing post-processing parameters without user understanding.
Innovation Solution
A system that automatically determines optimal configurations for machine learning pipelines on target devices by considering device characteristics and application constraints, using a graphical user interface to facilitate configuration and deployment, and optimizing post-processing parameters for precision and recall.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual configuration of machine learning models is performed, then customization to specific applications and devices is achieved, but time consumption and operational burden increase
Solution Approach 1:
The system performs self-configuration by automatically selecting pipeline components, hyperparameters, and post-processing parameters based on device characteristics and application constraints. The configuration service autonomously generates optimized pipeline configurations without requiring manual user input for each parameter, thereby reducing time consumption while maintaining adaptability.
Solution Approach 2:
The system automatically adjusts multiple parameters including hyperparameters, post-processing thresholds, and pipeline component selections based on device characteristics and application constraints. By systematically varying these parameters across different configurations and selecting optimal ones, the system achieves customized setups without manual parameter tuning.
2Measurement precision
If manual optimization of post-processing parameters is performed, then precision and recall can be tuned, but user understanding and control are required
Solution Approach 1:
The system incorporates feedback mechanisms where performance metrics such as precision and recall are automatically evaluated for different post-processing configurations. The configuration service uses this feedback to iteratively adjust parameters and select optimal configurations, eliminating the need for users to manually tune parameters while maintaining high measurement precision.
Solution Approach 2:
The configuration service acts as an intermediary between the user's application constraints and the actual post-processing parameter settings. It translates high-level constraints into specific parameter values automatically, so users don't need to directly configure complex post-processing parameters while still achieving optimized precision and recall.
3Reliability
If multiple pipeline configurations are evaluated, then optimal performance for device constraints is achieved, but configuration complexity increases
Solution Approach 1:
The configuration space is segmented into discrete pipeline components, hyperparameter sets, and post-processing parameters that can be independently selected and optimized. The configuration service evaluates combinations of these segmented components systematically, making the complex optimization process manageable and automated rather than manually complex.
Solution Approach 2:
The system performs preliminary evaluation of multiple pipeline configurations before final selection. By pre-evaluating configurations against device constraints and application requirements, the configuration service identifies optimal settings in advance, reducing the complexity of the final configuration selection process.
Data Source
AI summary
A system may configure a pipeline for a target device. The pipeline may include a signal processing component and a machine learning component. The pipeline may be configured to receive input data and generate output data based on the input data. For example, the output data may indicate detections in an output stream based on events in the input data in an input stream. The system may determine multiple post-processing configurations for post-processing the output data. A post-processing configuration may be configured to generate a detectable event based on the output data. The multiple post-processing configurations may be generated using a multi-objective optimization that varies one or more parameters for generating the detectable event.