Build a sharp prototype with real traction in Machine learning
Online · Machine learning · Hosted by Kaggle (Google)
Individual accounts; most competitions are global unless the host’s rules restrict jurisdictions.
Kaggle Competitions asks teams to build a sharper, more useful, and more credible solution in the machine learning space. Teams should choose a specific user problem and turn it into a concept or working demo that can be tested.
Judges will score not just the idea itself, but also user understanding, implementation realism, and the quality of the final presentation.
Registration
16 May – 16 Jun
Build window
17 Jun – 27 Jun
Submission
30 Jun
Judging
1 Jul – 6 Jul
Results
9 Jul
Total prize pool
Teams are responsible for the accuracy of their submissions, demos, and claims. Confirm the final rules and IP terms on the official registration page before you submit.
Confirm full rules and IP terms on the official registration site.
Cash
Mentorship
Other
Design the clearest core flow and value proposition for users in the machine learning space.
Judging focus
Clarity and ease of use.
Track sponsor
Kaggle (Google)
Create trustworthy, inclusive flows that hold up for a broader range of users.
Judging focus
Accessibility, trust, and practical use.
Track sponsor
Community partners
Show a concept that could be piloted today and scaled tomorrow.
Judging focus
Impact and implementation potential.
Track sponsor
Ecosystem allies
Team structure
Teams of 1–4 are welcome. Mixed teams across design, engineering, and research are encouraged.
Who can apply
The program runs as online participation. Students, recent graduates, and early-career builders are welcome.
Rules & conduct
Teams must clearly explain their work and tooling. Confirm the final official rules on the registration link before submitting.
How winners are chosen: Expert panel
Presentation
10%
Craft
10%
World-class mentors and judges from academia and industry.
Machine learning strategist
Product engineer
Design lead
Partner judge
Nandin-Erdene G.
Mentor, UX & research
Erkhembayar T.
Mentor, product & demo
Generative AI disclosure
If you used AI or automation, briefly explain where it was used, what data informed it, and any trust or risk tradeoffs.
Starter kits
Empowered by global partners
