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Related Experiment Video

Updated: Nov 18, 2025

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
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Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning.

Felix Mohr, Marcel Wever, Alexander Tornede

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |February 4, 2021
    PubMed
    Summary
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    This study introduces a method to predict machine learning pipeline runtimes, preventing timeouts in automated machine learning (AutoML) and improving resource efficiency for better model performance.

    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Automated machine learning (AutoML) aims to optimize machine learning pipelines for maximum prediction performance.
    • A significant challenge in AutoML is the inefficient use of computational resources, often leading to pipeline evaluations being canceled due to timeouts.
    • Evaluating numerous candidate pipelines is computationally expensive and can be unproductive if they fail to complete within allocated time.

    Purpose of the Study:

    • To develop an approach for predicting the runtime of two-step machine learning pipelines, including those with a pre-processor.
    • To enable automated systems to anticipate and avoid pipeline timeouts, thereby improving the efficiency of the AutoML process.
    • To enhance the successful evaluation of machine learning pipelines within computational constraints.

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    Main Methods:

    • Developing runtime models for individual algorithms used in machine learning pipelines.
    • Training separate offline runtime models for each potential algorithm.
    • Deriving an overall runtime prediction from the individual algorithm models for a complete pipeline.

    Main Results:

    • The proposed approach effectively predicts the runtime of machine learning pipelines.
    • It leads to an increase in the number of successfully completed pipeline evaluations in an AutoML tool.
    • The method preserves or enhances the quality of the best solutions found by the AutoML tool.

    Conclusions:

    • Runtime prediction is a viable strategy to mitigate timeouts in AutoML.
    • This approach optimizes computational resource utilization in automated machine learning.
    • The method contributes to more effective and efficient model development through AutoML.