Kangas is a smart data exploration, analysis, and model debugging tool for machine learning that was released by Comet.
Users may analyse and troubleshoot their data in a novel and incredibly intuitive way with Kangas, which is available on GitHub. By grouping, sorting, filtering, querying, and interpreting their structured and unstructured data, ML practitioners may use Kangas to create visualisations that are generated in real-time, accelerating the construction of models and generating useful information from their data.
When working with large-scale datasets, it can be overwhelming and time-consuming for data scientists to examine datasets both during the data preparation stage and model training. The technology enables intuitive real-time data exploration, debugging, and analysis to swiftly get insights and make faster, better decisions.
Following are the benefits that Kangas has to offer:
- Scalability: The tool was designed to perform well while handling huge datasets.
- Constructed with a specific purpose in mind: Computer Vision/ML concepts like scoring, bounding boxes, and more are supported right out of the box, and statistics and charts are generated automatically.
- Support for other media types: Kangas is not only restricted to conventional text inquiries. Additionally, it enables movies, photographs, and more.
- Interoperability: Kangas can be installed as a web app, a standalone local software, or even ran in a notebook. It ingests data in a straightforward format that makes it simple to integrate with any existing data science tooling.
- Open source: Kangas was created by and for the ML community and is completely open source.
Kangas was created to be used by the entire community, including academics, businesses, and researchers. They will be able to fully benefit from Kangas as individuals and teams seek to advance their ML ambitions. Due to its open source nature, anyone can contribute and improve it.
