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Comparison of Experiment Tracking Frameworks in Machine Learning Environments

Authors:
Tim Budras
Maximilian Blanck
Tilmann Berger
Andreas Schmidt

Keywords: Machine Learning; Experiment Tracking; Development Environment

Abstract:
The machine learning market is growing and machine learning is increasingly being used productively. Because of this, more and more tools have been developed in the past with the aim of supporting machine learning in practice. One type of these tools is called experiment tracking tools. Their objective is to keep track of the information generated by different experiment runs so that the information can be used later, for example, to find the best experiment run. Within the context of a bachelor thesis, a pre-selection of 20 systems was made and then 4 of them were selected for a more in-depth analysis and their characteristics were examined in more detail. This paper summarizes the most important findings of this thesis.

Pages: 21 to 28

Copyright: Copyright (c) IARIA, 2022

Publication date: May 22, 2022

Published in: conference

ISSN: 2308-4332

ISBN: 978-1-61208-969-0

Location: Venice, Italy

Dates: from May 22, 2022 to May 26, 2022