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OpenAtomGeneral MLAdvancedRegistration unverified

OpenTiny 前端 Web 应用开发挑战赛

A advanced general ml competition requiring roughly 25–70 hours for an end-to-end practice run.

Difficulty
Advanced
Estimated effort
25–70 hours
Registration deadline
To be confirmed
Compute
CPU only
OverviewPreparationSource

Overview

Who this competition fits

Advanced practitioners with experience in general machine learning. Requires strong technical foundations and comfort with complex ML pipelines.

Read the original official blurb
OpenTiny 是一套企业级 Web 前端开发解决方案,本赛题旨在通过TinyVue跨技术栈的架构,复用无渲染层逻辑并使用插件将Vue模板转化成对应技术栈的jsx函数式组件,最终开发出solid、react、openinula组件库。通过使用TinyEngine低代码引擎,开发低代码相关应用或者生态产品,推动低代码产品生态建设。 Official category: 实战竞技赛. Organizers: OpenTiny. Participants: 3.

Preparation

From registration to a first submission

  1. 01

    Python

  2. 02

    scikit-learn or similar ML library

  3. 03

    data analysis with pandas

Before you commit: Requires solid machine learning fundamentals and good experimental methodology. The competitive landscape is strong, with many experienced teams.

Source

How this page was assembled

Competition information is structured from the official page. Scores are platform estimates for decision support; official rules take precedence.

Official competition page
OpenAtom
Last checked
Jan 5, 2024

Entry assessment

est.
24

Prepare before joining

Overall fit estimate

Beginner fit24/100
Learning value72/100

Confirm eligibility and time commitment before moving into formal preparation.

Entry eligibility needs official confirmation

Open official pageSign in to saveOpen guide

Data and review state

Separate what is confirmed from what still needs review. Official rules and deadlines win.

View data stateClose
Source page verifiedMetric confirmedReviewed
Source published
Jan 5, 2024
Source created
Jan 5, 2024
Published on this site
Aug 1, 2026

Official metric is not published on the OpenAtom competition listing; verify scoring on the competition page.

reward_value_usd is a rough CNY→USD estimate (×0.14) for ranking only; use reward_summary for the official prize text.