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A Comprehensive Review of VertexML: A Model Training Platform
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Journal Article
A Comprehensive Review of VertexML: A Model Training Platform
2026
Overview
This paper presents a comprehensive survey of automated machine learning (AutoML) techniques, with a focus on meta-learning, hyperparameter optimization, and neural architecture search (NAS). Rather than proposing or evaluating a full AutoML platform, this work synthesizes insights from 49 influential research papers, organizing them into methodological categories and highlighting their contributions to the evolution of ML automation. The survey also analyzes trends across model types, optimization strategies, and publication patterns. Based on this review, the paper identifies research gaps and outlines key directions for future development of unified, scalable, and interpretable AutoML systems. This survey is intended to provide a structured foundation for researchers working toward improved ML automation pipelines.
Publisher
EDP Sciences
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