Proxy-Based Non-Iterative ML Pipeline for Fast Model Configuration
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Solution Overview
Problem
Current automated machine learning (AutoML) pipelines face challenges in efficiently configuring machine learning models for given training datasets due to the complexity of parameter interdependence and the cold-start problem, leading to costly and time-consuming iterative processes that are impractical for large datasets or projects with short time budgets.
Innovation Solution
The implementation of a proxy-based automatic non-iterative ML (PANI-ML) pipeline uses pre-configured proxy models to predict ML model configuration performance, narrowing the search space through algorithm selection, adaptive data reduction, and hyperparameter tuning, allowing for fast and accurate configuration of machine learning models without exhaustive searches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If iterative AutoML pipelines are used to configure machine learning models, then model configuration accuracy can be improved through exhaustive search, but the time consumption and computational cost increase significantly
Solution Approach 1:
The patent applies preliminary action by pre-training multiple proxy models on historical dataset-proxy pairs before actual model configuration. These proxy models are trained in advance to predict performance metrics, allowing the system to evaluate multiple configuration options rapidly without performing exhaustive iterative searches during the actual configuration phase, thus reducing time loss while maintaining accuracy
Solution Approach 2:
The patent introduces proxy models as intermediary components that mediate between the configuration parameters and the actual model performance evaluation. Instead of directly evaluating each configuration through time-consuming training, the proxy models serve as intermediaries that predict performance metrics, dramatically reducing the time required for configuration search while preserving accuracy
2Manufacturing precision
If exhaustive search is performed to find optimal model configuration, then configuration quality improves, but computational resources and processing time are excessively consumed
Solution Approach 1:
The patent applies partial action by using proxy models to evaluate a broader search space of configurations partially, rather than performing exhaustive evaluation on all possible configurations. The proxy models provide sufficient evaluation for most configurations, allowing the system to identify high-quality configurations without the excessive computational resources required for complete exhaustion of the search space
Solution Approach 2:
The patent creates copies of the evaluation function through proxy models. Instead of directly training and evaluating each candidate model configuration (which is computationally expensive), the system uses copied proxy models that have been pre-trained to predict performance. This copying approach maintains configuration quality while dramatically improving processing efficiency
3Reliability
If data science expertise is required to configure machine learning models, then model performance can be optimized, but the process becomes unsustainable and non-scalable due to scarcity of experts
Solution Approach 1:
The patent implements self-service by creating an automated system that performs model configuration without requiring human data science expertise. The proxy-based pipeline automatically selects configurations, evaluates them through pre-trained proxy models, and identifies optimal settings, enabling the system to serve itself and eliminating dependence on scarce expert resources while maintaining reliable model performance and achieving scalability
4Measurement precision
If iterative optimization processes are used for model configuration, then convergence to optimal solution can be achieved, but the process becomes impractical for large datasets or projects with short time budgets
Solution Approach 1:
The patent applies preliminary action by pre-training proxy models on diverse datasets and configurations before the actual optimization task. This preliminary preparation allows the proxy models to rapidly evaluate configurations during the actual process, achieving convergence to optimal solutions without the prolonged duration characteristic of iterative processes on large datasets
Solution Approach 2:
The patent changes the evaluation parameter from direct model training time to proxy model prediction time. By transforming the evaluation metric and using pre-trained proxy models, the system maintains optimization accuracy while dramatically reducing the duration required, making the process practical for large datasets and time-constrained projects
Data Source
AI summary
A proxy-based automatic non-iterative machine learning (PANI-ML) pipeline is described, which predicts machine learning model configuration performance and outputs an automatically-configured machine learning model for a target training dataset. Techniques described herein use one or more proxy models—which implement a variety of machine learning algorithms and are pre-configured with tuned hyperparameters—to estimate relative performance of machine learning model configuration parameters at various stages of the PANI-ML pipeline. The PANI-ML pipeline implements a radically new approach of rapidly narrowing the search space for machine learning model configuration parameters by performing algorithm selection followed by algorithm-specific adaptive data reduction (i.e., row- and/or feature-wise dataset sampling), and then hyperparameter tuning. Furthermore, because of the one-pass nature of the PANI-ML pipeline and because each stage of the pipeline has convergence criteria by design, the whole PANI-ML pipeline has a novel convergence property that stops the configuration search after one pass.