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Kedro-Viz

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Visualise your Kedro data and machine-learning pipelines and track your experiments.

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OSS

Kedro-Viz

Added 1 June 2026

#data-visualization #experiment-tracking #hacktoberfest #kedro #kedro-plugin #python #react

Overview

Kedro-Viz is an open-source JavaScript tool that visualizes Kedro data and machine-learning pipelines. It displays pipeline structure and tracks experiment runs, helping developers understand data flow and model performance.

Best for

Best for
Data scientists and ML engineers using Kedro who need pipeline visualization and experiment tracking.

Use cases

  • Visualizing complex Kedro pipeline DAGs for debugging and documentation.
  • Tracking and comparing experiment runs to monitor model performance.
  • Exploring data dependencies and node connections in ML workflows.

Notes

Kedro-Viz is an open-source JavaScript tool that visualizes Kedro data and machine-learning pipelines. It displays pipeline structure and tracks experiment runs, helping developers understand data flow and model performance.

749 stars on GitHub. Last updated 2026-05-29. Licensed Apache-2.0.

Use cases

  • Visualizing complex Kedro pipeline DAGs for debugging and documentation.
  • Tracking and comparing experiment runs to monitor model performance.
  • Exploring data dependencies and node connections in ML workflows.

Pros

  • Free and open source with an active community.
  • Integrates natively with Kedro, requiring minimal setup.
  • Provides clear, interactive visualizations of pipeline structure.

Cons

  • Limited to Kedro-based projects, not a general-purpose visualization tool.
  • Experiment tracking features are less comprehensive than dedicated platforms.
  • Dependency on Kedro’s ecosystem may restrict adoption outside that framework.

Indexed from awesome-llmops and enriched against its public facts.

Pros

  • Free and open source with an active community.
  • Integrates natively with Kedro, requiring minimal setup.
  • Provides clear, interactive visualizations of pipeline structure.

Cons

  • Limited to Kedro-based projects, not a general-purpose visualization tool.
  • Experiment tracking features are less comprehensive than dedicated platforms.
  • Dependency on Kedro's ecosystem may restrict adoption outside that framework.