Visualization Tools for Machine Learning Pipelines: A Review

The increasing complexity and adoption of machine learning (ML) pipelines has led to a rising demand for effective visualization tools. This paper presents a comprehensive review of existing tools for visualizing data flow in machine learning (ML) pipelines. We highlight the tools' purposes, in...

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Vydané v:IEEE Pacific Visualization Symposium s. 399 - 407
Hlavní autori: Golendukhina, Valentina, Felderer, Michael, Sonnleithner, Lisa
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 22.04.2025
Predmet:
Complexity theory
Computing methodologies-Machine Learning
Data visualization
Directed acyclic graph
Human-centered computing-Visualization-Visualization systems and tools
Machine learning
Monitoring
Pipelines
Reviews
Usability
Visual systems
Visualization
ISSN:2165-8773
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Popis
Shrnutí:The increasing complexity and adoption of machine learning (ML) pipelines has led to a rising demand for effective visualization tools. This paper presents a comprehensive review of existing tools for visualizing data flow in machine learning (ML) pipelines. We highlight the tools' purposes, integration methods, and visualization techniques. We collected and analyzed 22 open-source tools and concepts, analyzing their features and classifying them based on their primary purpose. Our analysis revealed five main purposes of visualization tools: exploration, explanation, visual development, comparison, monitoring. We provide an analysis of their integration methods, from standalone visual interfaces to code-level libraries, as well as a review of various visualization techniques, including Directed Acyclic Graphs (DAGs), pipeline matrices, and annotated visualizations. Our findings highlight the importance of visualization in enhancing the interpretability and efficiency of ML workflows. Moreover, the paper provides key limitations and challenges in current visualization methods to promote future research directions enhancing the usability and functionality of ML pipeline visualization tools.
ISSN:2165-8773
DOI:10.1109/PacificVis64226.2025.00049