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  3. open-eBackup开源备份软件实战竞技赛
OpenAtomGeneral MLAdvancedRegistration unverified

open-eBackup开源备份软件实战竞技赛

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
本赛事由华为数据存储联合承办,open-eBackup是国内首个开源的兼容多应用生态的备份软件,不仅能够对接数据库、虚拟化、文件等多场景,并且具有安全、重删、压缩等功能。希望能够通过大赛,吸引全球备份软件用户、伙伴、开发者共同加入,共同探索数据备份技术发展趋势,制定相应的软件解决方案。 Official category: 实战竞技赛. Organizers: 华为技术有限公司. Participants: 10.

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
Oct 8, 2024

Entry assessment

est.
19

Prepare before joining

Overall fit estimate

Beginner fit19/100
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
Oct 8, 2024
Source created
Oct 8, 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.