01 / CONTEXT
A correct score requires much more than an upload form.
In an ML competition, each participant submits predictions built on the same problem, and the system must validate the format, run the appropriate evaluator, protect the test data, and update the ranking in a reproducible way.
Beyond evaluation, the platform must link problem description, resources, submission history, statistics and competition administration.
02 / INT80 ROLE
Product development in the ONIA technical team.
Liviu Popa and INT80 contribute to the software component of the ONIA ecosystem and the development of the MLCompete platform together with the technical team: from participant experience to evaluation and operation logic.
- 01
Competition cycle
Modeling the stages between problem publication, registration, submissions and ranking closure.
- 02
Evaluation pipeline
Validation, specific evaluators and reproducible results for each problem.
- 03
Participant experience
Problems, resources, history and feedback organized into one cohesive product.
- 04
Operations
Roles, administration, statistics and tools needed by the competition team.
03 / PLATFORM MAP
A complete workflow for ML competitions.
Problems & data
Statement, resources, rules and datasets organized by competition.
Submit & Validate
Predictable file flow, format and trial history.
Automatic evaluation
Isolated evaluators that apply the right metrics to each problem.
Ranking
Leaderboards and statistics that make progress visible and comparable.
04 / RESULT
Reusable infrastructure for competitive learning.
MLCompete gives the technical team a common foundation for launching and operating competitions, and participants a transparent environment in which to iterate, measure and learn.