Uber Open-Sourced ‘Manifold’: A Visual Debugging Tool for Machine Learning

Uber recently open-sourced its model-agnostic visual debugging tool ‘Manifold’ for machine learning models. The objective of this tool is to help data scientists and data engineers identify performance issues across datasets and models in a visually intuitive way. 

Machine learning applications are different than general software applications in terms of their constantly changing and evolving structure as the model builds more knowledge. Therefore debugging and interpreting machine learning models has become one of the most challenging roles of real-world AI solutions. Performance issues across ML data slices and models can be easily identified using Manifold.

Features in Version-1 Release

  • Model-agnostic support for general binary classification and regression model debugging.
  • Visualization support for tabular feature input including numerical, categorical, and geospatial feature types.
  • Integration with Jupyter Notebook.
  • Interactive data slicing and performance comparisons based on per-instance prediction loss and other feature values.

Github: https://github.com/uber/manifold

Paper (2018): https://arxiv.org/pdf/1808.00196.pdf

Demo Web: http://manifold.mlvis.io/

Asif Razzaq is the CEO of Marktechpost Media Inc.. As a visionary entrepreneur and engineer, Asif is committed to harnessing the potential of Artificial Intelligence for social good. His most recent endeavor is the launch of an Artificial Intelligence Media Platform, Marktechpost, which stands out for its in-depth coverage of machine learning and deep learning news that is both technically sound and easily understandable by a wide audience. The platform boasts of over 2 million monthly views, illustrating its popularity among audiences.

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