301 Interest Score
117 Discussions
0.39 Engagement
Aug 2021 Launched

Evidently helps evaluate and monitor machine learning models in production. The tool generates visual reports on model performance to detect and debug issues. Follow the releases on Github: https://github.com/evidentlyai/evidently

What the Community Said

BRAVO! The open source is an exceptionally shrewd, solid, STRONG business move ... basically demonstrates, in a compelling manner, a justifiable confidence on the part of founders and investors that the best is yet to come, ie company can solve much dicier problems for clients. The beauty of this strategy is that simultaneously disrupts the business model of competitors who are now showing that they cannot compete and fear competing in the future. Those who rely upon the proprietary source busin

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Hi Emeli and Elena, you did awesome job!!!!! It really takes a lot to make such product freely available to data community. I have made my university project by using Evidently and going to present it to large set of people. Specifically I will mention about this library and your efforts behind it. Just one thing from your side will be of great help, I want to change histogram bar colors from red to green on Data Drift page for all features, so that it will looks good on my project theme. I rese

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I really believe in Evidently.AI! Elena and Emeli are great entrepreneurs and Evidently.AI really nails the problem of monitoring and sustain ML models.

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Sounds like a great idea for products based on machine learning!

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#3 Product of the Day! Congrats @elenasamuylova and @emeli_dral

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Frequently Asked Questions

A measure of community engagement at launch. Higher means more people noticed and interacted with the product. It's a traction signal, not a quality rating.

Discussion threads divided by interest score. Above 0.30 is strong. Below 0.15 suggests the product got clicks but not conversation.

Categories come from the product's launch tags. Most products appear in 2-3 categories. The primary category is listed first.

The scores reflect launch-period engagement. Historical data is preserved and doesn't change retroactively. The build date at the bottom shows when the index was last refreshed.

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