Pipeline Graph for Scalable ML Model Discovery
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Solution Overview
Problem
Current machine learning model exploration methods are inefficient and computationally intensive, requiring substantial resources to determine optimal predictive modeling pipelines, especially when dealing with large datasets and multiple hyperparameters.
Innovation Solution
The implementation of a pipeline graph system that uses heuristic-based methods to generate and compare alternative predictive modeling pipelines, applying hyperparameter optimization and parallel execution to identify leader pipelines efficiently, allowing for user-defined metrics and adaptability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If exhaustive pipeline exploration is performed to find optimal predictive modeling pipelines, then model accuracy is improved, but computational resource consumption increases significantly
Solution Approach 1:
The patent segments the pipeline exploration process into multiple phases: initial exploration phase that evaluates pipelines with default hyperparameters, and subsequent refinement phases that focus hyperparameter optimization only on promising pipelines. This segmentation allows the system to quickly filter out poor pipelines before investing computational resources in thorough hyperparameter search, thereby maintaining model accuracy while reducing overall computational resource consumption.
Solution Approach 2:
The patent applies preliminary action by performing initial pipeline evaluation with default hyperparameters before conducting full hyperparameter optimization. This preliminary filtering step identifies a subset of promising pipelines that warrant further optimization, preventing wasteful computation on pipelines that would not achieve good performance regardless of hyperparameter tuning. This approach ensures computational resources are concentrated on pipelines with higher potential accuracy.
2Measurement precision
If comprehensive hyperparameter optimization is applied to all pipelines, then predictive model performance is improved, but exploration time increases
Solution Approach 1:
The patent divides hyperparameter optimization into staged phases: initial broad exploration with limited optimization, followed by focused refinement on top-performing pipelines. This segmentation enables the system to achieve satisfactory model performance across a broad set of pipelines quickly, then invest more time in optimizing only the most promising candidates, thereby improving overall performance while controlling total exploration time.
Solution Approach 2:
The patent applies partial action by performing hyperparameter optimization selectively on a subset of pipelines identified as promising through initial evaluation, rather than applying exhaustive optimization to all pipelines. This selective approach achieves near-optimal performance for the final model while significantly reducing the total time required for pipeline exploration compared to comprehensive optimization of all possible pipelines.
3Adaptability or versatility
If the pipeline graph includes all possible machine learning components and configurations, then model exploration completeness is improved, but system complexity increases
Solution Approach 1:
The patent segments the pipeline graph into modular layers, where each layer represents a specific type of machine learning component or operation. This modular segmentation allows the system to maintain completeness by including all necessary component types while managing complexity through structured organization. The segmentation also enables selective exploration of different layer combinations based on the specific modeling task, reducing the effective search space without sacrificing exploration completeness.
Solution Approach 2:
The patent implements dynamic pipeline graph construction where the graph structure and included components are adapted based on the specific predictive modeling task and available data characteristics. Rather than statically including all possible components, the system dynamically determines which components and configurations are relevant, maintaining exploration completeness for the given context while reducing system complexity by excluding irrelevant elements.
4Productivity
If multiple pipeline variants are evaluated in parallel, then discovery speed is improved, but resource requirements increase
Solution Approach 1:
The patent segments the parallel evaluation process into batches or waves, where pipelines are evaluated in groups rather than all simultaneously. This segmentation allows the system to achieve high discovery speed through parallel processing while managing resource requirements by controlling the degree of parallelism. The segmented approach also enables progressive refinement where resources are allocated to subsequent batches based on performance feedback from previous batches.
Data Source
AI summary
A computer implemented method includes generating a pipeline graph having a plurality of layers, each of the plurality of layers having one or more machine learning components for performing a predictive modeling task. A plurality of pipelines are operated through the pipeline graph on a training dataset to determine a respective plurality of results. Each of the plurality of pipelines are distinct paths through selected ones of the one or more machine learning components at each of the plurality of layers. The plurality of results are compared to known results based on a user-defined metric to output one or more leader pipelines.