Automated Machine Learning Pipeline Generation via Grammar
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Solution Overview
Problem
The existing methods for generating machine learning pipelines are inefficient and require manual intervention, as they often rely on trial-and-error approaches and are limited to specific domain spaces, making them time-consuming and resource-intensive.
Innovation Solution
An automated system that uses a pipeline grammar to generate and optimize machine learning pipelines by selecting appropriate modules based on defined objectives, employing algorithms like limited discrepancy search and best-first search to iteratively improve pipeline performance using a fraction of the dataset.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual trial-and-error approaches are used to generate machine learning pipelines, then flexibility in pipeline design is maintained, but time consumption and resource intensity increase significantly
Solution Approach 1:
The system enables self-service automated pipeline generation by using the provided grammar and objective to automatically search, select, and configure machine learning modules without requiring manual trial-and-error intervention. The automated search process independently explores the pipeline space and instantiates optimal pipelines based on the defined objective.
2Reliability
If comprehensive search of pipeline space is performed to achieve optimal performance, then pipeline quality improves, but computational costs and processing time increase
Solution Approach 1:
The system changes the search parameter by using a fraction of the dataset (e.g., 10% to 100%) to evaluate pipeline candidates during the automated search process. This allows comprehensive exploration of the pipeline space with reduced computational cost, and the selected pipeline can be validated on the full dataset to ensure optimal performance.
Solution Approach 2:
The system performs preliminary action by using a subset of the data to pre-evaluate and select promising pipeline configurations before final validation. This preliminary search through a fraction of the data reduces the overall computational burden while still identifying high-quality pipeline candidates.
3Adaptability or versatility
If domain-specific constraints are applied to pipeline generation, then pipeline relevance to specific tasks improves, but search space exploration is limited
Solution Approach 1:
The system achieves universality by using a parameterized grammar structure that can represent multiple domain-specific pipeline types through a unified framework. The grammar and objective parameters can be configured to suit different domains (e.g., image classification, text processing), allowing the same automated search system to generate relevant pipelines across various tasks without domain-specific customization of the search algorithm.
Data Source
AI summary
Systems, computer-implemented methods, and computer program products to facilitate automated generation of a machine learning pipeline based on a pipeline grammar are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a pipeline structure generator component that generates a machine learning pipeline structure based on a pipeline grammar. The computer executable components can further comprise a pipeline optimizer component that selects one or more machine learning modules that achieve a defined objective to instantiate a machine learning pipeline based on the machine learning pipeline structure.