Machine-Learning Pipeline Integration for Multi-Objective Search
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Solution Overview
Problem
Creating an effective machine-learning pipeline is a multi-objective optimization problem due to conflicting objectives and side-effects among emerging pipeline stages, such as hyperparameter tuning and compression, which require an efficient system and method for integration.
Innovation Solution
A system and method for constructing machine-learning pipelines that enumerate valid integrated pipelines, collect metrics, reduce them to a single metric, and select the optimal pipeline based on the reduced metric, using techniques like constraint relaxation, aggressive pruning, and empirical search.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple pipeline value-adds (hyperparameter tuning, compression, de-biasing) are integrated into the training pipeline, then the model performance and security are improved, but the pipeline complexity increases
Solution Approach 1:
The patent segments the pipeline into base pipeline and value-add pipeline components, allowing independent development and testing of each module before integration. This segmentation enables complex pipeline value-adds to be managed as discrete, testable units rather than a monolithic complex system.
Solution Approach 2:
The patent implements preliminary validation of pipeline value-adds before they are integrated into the base pipeline. By validating value-adds in isolation first, the system ensures correctness and safety before complexity is accumulated, preventing harmful interactions from compromising reliability.
2Manufacturing precision
If pipeline value-adds are added to improve specific objectives (accuracy, size reduction), then the target metric is improved, but conflicting objectives create adverse side-effects
Solution Approach 1:
The patent implements feedback mechanisms where pipeline value-adds report their impact on multiple objectives, including adverse side-effects. This feedback loop enables the system to detect when a value-add improves one metric while harming another, allowing for dynamic adjustment or rejection of conflicting value-adds.
Solution Approach 2:
The patent applies preliminary validation that anticipates and prevents adverse side_effects before they occur in the integrated pipeline. By testing value-adds in isolation and validating their behavior beforehand, the system prevents conflicting objectives from creating harmful interactions during actual pipeline execution.
3Reliability
If exhaustive search of all possible pipeline integrations is performed, then the optimal pipeline is guaranteed to be found, but the computational time required becomes prohibitive
Solution Approach 1:
The patent applies partial validation by testing a representative subset of pipeline value-adds and configurations rather than exhaustively searching all possibilities. This partial action approach provides sufficient confidence in pipeline optimality without the prohibitive computational cost of complete exhaustive search.
Solution Approach 2:
The patent uses virtualization and sandboxing to create isolated copies of pipeline value-adds for validation. By working with virtual copies rather than actual pipeline executions, the system can perform extensive validation and comparison without the time cost of running complete pipeline integrations for every possible configuration.
Data Source
AI summary
A system, apparatus and a method for constructing pipelines, including enumerate a subspace of valid integrated pipelines, collecting a set of metrics for each of the plurality of pipelines, reducing the set of metrics to a single metric, selecting a final integrated pipeline from among the valid integrate pipelines based on reduced metric.