Machine Learning Pipeline Hyper-Parameter Optimization Via Root Cause Analysis
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Solution Overview
Problem
Optimizing the hyper-parameter space in machine learning data processing pipelines is challenging due to the complexity and rarity of issues such as stalls, excessive resource consumption, and failures, especially when identifying the root causes of these problems is difficult.
Innovation Solution
A system and method for hyper-parameter space optimization that collects data on the execution of the pipeline, generates structured reports to analyze root causes, and determines corrective actions based on this data, using techniques like Bayesian hyper-parameter optimization to identify optimal hyper-parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional hyper-parameter optimization methods are used, then the pipeline may complete training, but it suffers from stalls, excessive resource consumption, and failures due to inadequate root cause analysis
Solution Approach 1:
The patent segments the hyper-parameter optimization process into distinct phases: initial training phase, monitoring phase for detecting stalls and failures, root cause analysis phase, and corrective action phase. This segmentation allows the system to address specific issues at appropriate stages rather than treating the entire process as a single black box, thereby improving reliability while managing complexity.
Solution Approach 2:
The patent implements continuous feedback mechanisms by monitoring pipeline execution metrics during training, comparing actual performance against expected performance, and using this feedback to identify root causes of stalls and failures. This feedback loop enables dynamic adjustment of hyper-parameters based on real-time observations, improving reliability without requiring exhaustive exploration of the entire hyper-parameter space.
2Measurement precision
If exhaustive hyper-parameter search is performed to ensure optimal performance, then model accuracy improves, but training time and resource consumption increase significantly
Solution Approach 1:
The patent performs preliminary actions by establishing performance baselines and identifying critical hyper-parameters before full-scale optimization. By pre-processing the hyper-parameter space to identify which parameters have the most significant impact on model accuracy, the system can focus optimization efforts on those specific parameters, achieving high accuracy without exhaustive search of all parameters.
Solution Approach 2:
The patent dynamically changes hyper-parameters based on observed pipeline behavior and root cause analysis results. Instead of fixed exhaustive search, the system adaptively adjusts hyper-parameters in response to detected issues such as stalls or failures, allowing it to achieve optimal accuracy while avoiding time-consuming trials of clearly suboptimal configurations.
3Productivity
If hyper-parameters are not optimized, then training completes quickly, but the pipeline experiences frequent stalls, failures, and excessive resource consumption
Solution Approach 1:
The patent applies partial optimization by focusing on the most critical hyper-parameters that have the greatest impact on pipeline stability rather than optimizing all parameters equally. This partial action approach achieves sufficient reliability improvement without requiring the time investment of complete optimization, thus maintaining acceptable training speed while reducing stalls and failures.
4Reliability
If root cause analysis is performed on every pipeline failure, then execution reliability improves, but the complexity of the optimization system increases
Solution Approach 1:
The patent implements preliminary anti-action by establishing performance baselines and detection thresholds before pipeline execution. When metrics deviate from these pre-established baselines, the system triggers root cause analysis. This approach prevents the need for continuous complex analysis by only activating detailed diagnostics when necessary, thereby improving reliability while controlling system complexity.
Data Source
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AI summary
A data processing pipeline may be generated to include an orchestrator node, a preparator node, and an executor node. The preparator node may generate a training dataset. The executor node may execute machine learning trials by applying, to the training dataset, a machine learning model and/or a different set of trial parameters. The orchestrator node may identify, based on a result of the machine learning trials, a machine learning model for performing a task. Data associated with the execution of the data processing pipeline may be collected for storage in a tracking database. A report including de-normalized and enriched data from the tracking database may be generated. The hyper-parameter space of the machine learning model may be analyzed based on the report. A root cause of at least one fault associated with the execution of the data processing pipeline may be identified based on the analysis.