#98 · Primary category: MLOps & Evaluation

EvalAI

ai ai-challenges angularjs artificial-intelligence challenge codecov coveralls django docker evalai evaluation evaluation-framework github-actions leaderboard machine-learning python reproducibility reproducible-research

:cloud: :rocket: :bar_chart: :chart_with_upwards_trend: Evaluating state of the art in AI

Project last updated:08/29/26

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Why we included this project

Running an AI competition is fiddlier than it looks. Participants need somewhere to submit models, the backend has to execute and score them without surprises, and the leaderboard has to be dependable enough that researchers can cite results from it. EvalAI is a self-hostable platform that handles all of that: challenge organizers get a submission interface, scoring backends that run against fixed evaluation protocols, and a public leaderboard, so they do not have to build their own evaluation infrastructure from scratch. Paper results are hard to reproduce because small differences in dataset splits, metrics, or how an algorithm was coded creep in, and fixed evaluation protocols keep those variables under control. If your group runs internal model bake-offs, a course final project, or a public benchmark, EvalAI gives you the operational setup to keep those comparisons fair and auditable.

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