Dingo is a comprehensive data quality evaluation tool designed to address challenges in managing data quality for both tabular and text/LLM data in machine learning projects.
Python, Machine Learning, Data Quality Evaluation
2025-03-12
- website
Highlight 1
Dingo offers seamless support for both tabular and textual data, making it versatile for various types of ML projects.
Highlight 2
The tool provides an intuitive user interface which simplifies the process of assessing and improving data quality.
Highlight 3
Its online demo allows potential users to quickly assess Dingo's capabilities without any setup, enhancing user onboarding.
Improvement 1
There is a need for more thorough documentation to assist new users in fully utilizing all features.
Improvement 2
Expanding the range of data quality checks and metrics available would increase the tool’s applicability.
Improvement 3
Improving responsiveness and performance for large datasets could enhance user experience.
Product Functionality
Enhance the product by adding more customizable quality checks and metrics for user-defined data quality rules.
UI & UX
Improve the UI/UX by simplifying navigation and integrating tooltips or guided tours for first-time users.
SEO or Marketing
Increase visibility through SEO optimizations, such as creating informative blog posts about data quality and use cases for Dingo.
MultiLanguage Support
Consider adding multi-language support to cater to a global audience, which would include translating documentation and the user interface.
- 1
What types of data can Dingo evaluate?
Dingo can evaluate both tabular and textual data, making it suitable for a variety of data types in machine learning projects.
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Does Dingo provide an online demo?
Yes, Dingo offers an online demo which you can try out at www.huggingface.co/spaces/DataEval/dingo to get a feel for the tool.
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How do I get started with Dingo?
You can get started with Dingo by accessing the online demo, or you can clone the repository from GitHub to set it up locally.