Comet Releases Kangas to Support Deep Machine Learning for Data Scientists

Comet, provider of an MLOps platform for machine learning (ML) teams from startup to enterprise, is releasing a new product: Kangas, open sourced to democratize large scale visual dataset exploration and analysis for the computer vision and machine learning community.

Kangas helps users understand and debug their data in a new and highly intuitive way, according to the company. 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.

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 datasets of any scale into clear visualizations, according to the vendor.

“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 dramatically improves the experience for ML practitioners.”

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 datasets.

With Kangas, data scientists can query their large-scale datasets 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:

  • Unparalleled Scalability: Kangas was developed to handle large datasets with high performance.
  • Purpose Built: Computer Vision/ML concepts like 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. It 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 tooling data scientists already use.
  • Open Source: Kangas is 100% open source and is built by and for the ML community.

Kangas was designed for the entire community, 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.

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