Automatic Pipeline Parameter Tuning via Iterative Feedback
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Manual tuning of parameters in processing pipelines is time-consuming, labor-intensive, and prone to human errors, leading to suboptimal performance.
Innovation Solution
An automatic pipeline tuning system that automatically optimizes parameters in a pipeline by accepting input representations of the pipeline and datasets, using preprocessing, parameter search, and evaluation modules to find optimal parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual parameter tuning is performed, then developers can control pipeline behavior, but the process becomes time-consuming and labor-intensive
Solution Approach 1:
The system enables self-service automation where the pipeline automatically performs parameter tuning without human intervention. The automation module iteratively modifies parameters, executes the pipeline, and evaluates results autonomously, freeing developers from manual tuning tasks while achieving optimal parameters efficiently
Solution Approach 2:
The patent replaces the mechanical manual process of parameter tuning with an automated computational system. The automation module uses algorithms to systematically explore parameter spaces and evaluate pipeline performance, substituting human manual operations with automated computational processes that are faster and more consistent
2Measurement precision
If manual parameter tuning is performed, then developers can assess output, but significant resource capacity is utilized
Solution Approach 1:
The automation module performs partial exploration of the parameter space by focusing on the most promising parameter combinations identified through iterative evaluation. Rather than exhaustively testing all possible parameters, the system performs targeted adjustments and assessments, reducing overall resource consumption while maintaining effective tuning
3Ease of operation
If manual parameter tuning is performed, then developers can modify parameters, but the pipeline operates at less than optimal efficiency
Solution Approach 1:
The system implements a feedback loop where the automation module continuously monitors pipeline output quality and uses this information to guide subsequent parameter adjustments. The evaluation module assesses each parameter configuration's impact on output, and this feedback directs the next tuning iterations, ensuring the pipeline converges toward optimal efficiency
Data Source
AI summary
Approaches are disclosed that can automatically tune parameters in an application pipeline. An application pipeline and datasets with labels can be accepted as input. An application pipeline can include various modules and information such as their interconnections and a set of parameters to be tuned. The input data can be fed into a preprocessing module and then fed into a parameter search module, which can navigate through the parameter space and search for improved and/or optimal parameters. The search can progress to informed selections based on outcomes of previous evaluations. The parameters identified can be used by an execution module to execute the pipeline. The results produced can be evaluated by an evaluation module that condenses its findings into a single score, which is passed back to a parameter search module to inform the next round of parameter predictions. Such an iterative process can continue until certain criteria are met, with final output corresponding to a set of automatically tuned parameters.