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OpenAtomGeneral MLAdvancedRegistration unverified

OpenTenBase开源核心贡献挑战赛

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
OpenTenBase是腾讯云数据库团队在 PostgreSQL基础上研发的企业级分布式HTAP开源数据库,为了满足复杂多样的业务场景以及外部商用的企业级场景需求,更好凝聚产业上下游力量共建基础能力,秉持着“开源开放合力共创”的理念,通过开展本赛项,与上下游用户、开发者、行业伙伴共同构建一个开放繁荣的数据库生态,降低用户使用门槛,给用户带来创新价值。 Official category: 实战竞技赛. Organizers: 腾讯云. Participants: 199.

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 10, 2024

Entry assessment

est.
18

Prepare before joining

Overall fit estimate

Beginner fit18/100
Learning value71/100

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 10, 2024
Source created
Jan 10, 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.