Challenges and complexities in developing a Debugger-like tool for Real-Time insights into Machine Learning Model Training

Authors: Vishakha Agrawal

DOI: https://doi.org/10.5281/zenodo.14684742

Short DOI: https://doi.org/g82ccd

Country: USA

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Abstract: Developing debugging tools for machine learning (ML) model training poses significant technical challenges and architectural complexities. This paper delves into the unique demands of real-time monitoring and analysis of neural net- work training, revealing the limitations of traditional debugging approaches in ML contexts. We propose innovative solutions to overcome these challenges, highlighting the critical intersection of distributed systems, performance optimization, and ML observability. Our research provides valuable insights into the design of effective debugger-like tools, enabling data scientists and engineers to gain deeper real-time insights into ML model training processes.

Keywords: Debugging, TensorBoard, MLFlow, Weights Biases, State Management

Paper Id: 232035

Published On: 2020-04-04

Published In: Volume 8, Issue 2, March-April 2020

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