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Comet Introduces Tool for Machine Learning

Kangas is an open source smart data exploration, analysis, and model-debugging tool for machine learning.

Note: TDWI’s editors carefully choose vendor-issued press releases about new or upgraded products and services. We have edited and/or condensed this release to highlight key features but make no claims as to the accuracy of the vendor's statements.

Comet, provider of an MLOps platform for teams, announced a new product: Kangas. Open sourced to democratize large-scale visual data set exploration and analysis for the computer vision and machine learning community, Kangas helps users understand and debug their data in a new and intuitive way. With Kangas, visualizations are generated in real time, enabling ML practitioners to group, sort, filter, query, and interpret their structured and unstructured data to derive meaningful information and accelerate model development.

Data scientists often need to analyze large data sets both during the data preparation stage and during model training, which can be overwhelming and time-consuming. Kangas makes it possible to intuitively explore, debug, and analyze data in real time to quickly gain insights, leading to better, faster decisions. With Kangas, users are able to transform data sets of any scale into clear visualizations. 

“A key component of data-centric machine learning is being able to understand how your training data impacts model results and where your model predictions are wrong,” said Gideon Mendels, CEO and co-founder of Comet. “Kangas accomplishes both of these goals and improves the experience for ML practitioners.”

Putting Large-Scale Machine Learning Data Set Analysis at Your Fingertips

Developed with the unique needs of ML practitioners in mind, Kangas is a scalable, dynamic, and interoperable tool that allows for the discovery of patterns buried deep within oceans of data sets. With Kangas, data scientists can query their large-scale data sets in a manner that is natural to their problem, allowing them to interact and engage with their data in novel ways. 

Noteworthy benefits of Kangas include:

  • Scalability: Kangas was developed to handle large data sets with high performance.
  • Purpose-built: Computer vision and ML concepts such as scoring, bounding boxes, and more are supported out-of-the-box, and statistics/charts are generated automatically.
  • Support for different forms of media: Kangas is not limited to traditional text queries, but also supports images, videos, and more.
  • Interoperability: Kangas can run in a notebook, as a standalone local app, or even deployed as a web app. It ingests data in a simple format that makes it easy to work with whatever tools data scientists already use.
  • Open source: Kangas is 100 percent open source and is built by and for the ML community.

Kangas was designed to be embraced by students, researchers, and the enterprise. As individuals and teams work to further their ML initiatives, they will be able to leverage the full benefits of Kangas. Being open source, all are able to contribute and further enhance it as well.

Kangas is available as an open source package for any type of use case. It will be available under Apache License 2 and is open to contributions from community members. Learn more at

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