The Open-source Machine Learning Database OpenMLDB Contributor Program is Fully Launched!

[Without Open-source, There Will Be No AI]

The spirit of open-source has provided an important driving force for the rapid development of artificial intelligence in the recent ten years. With the continuous open-source of AI technologies such as computing framework and algorithm, the threshold of AI model construction has been reduced. However, the industrialization of AI needs to optimize all links of the whole process of machine learning, such as data processing, feature engineering, model construction and application online. Such open-source AI basic technologies are rare in the market.

The open-source of machine learning database OpenMLDB provides efficient and correct data for machine learning applications, accelerates the engineering landing of AI applications, and fills the gap in the field of open-source AI basic technology. The comprehensive development of OpenMLDB is due to the support of the developers. With the release of the OpenMLDB version 0.3, the OpenMLDB community launched the “OpenMLDB Contributor Program” (OCP) in the hope that more developers will participate in the community construction, build an inclusive, friendly, and perfect open-source ecology together, and accelerate the implementation process of AI engineering.

“OCP OpenMLDB Contributor Program”


All developers that are interested in the OpenMLDB project

Open-source Community:


Contributor Task (phase I):

See GitHub link (Contributor Challenges — Collection 1) for the list of contributor tasks

Contributor Challenges — Collection 1​

Specific Participation Instructions:

Step 1: In the contributor’s planned task list, select the issue of interest and leave a message “I would like to help”. If you leave a message, it will be deemed that the task has been claimed successfully.

Step 2: Develop based on issue and submit a pull request. Note that after the PR is submitted, please link to the PR you submitted in “Linked pull requests” on the relevant issue page.

Step 3: After the PR is submitted, the OpenMLDB PMC will review, and the PR that meets the task standard will be merged. After merging, the task will be regarded as completed.

If you encounter any problems during this period, you can communicate in the OpenMLDB technology exchange group (you can click on the link below to scan the code to join).

OpenMLDB Technical exchange group ​

Detailed Rules:

· Contributor task: focus on the top issue — “Contributor Challenges — Collection 1

· Claim method: if you leave a message “I would like to help” under the issue of the project, it will be regarded as a successful claim

· Completion criteria: The first to submit and merge the PR is regarded as task completed

· Communication methods: GitHub issue, slack and OpenMLDB technical exchange group

Contribution Reward:

· The tasks in this phase are simple tasks. After the task is completed, you will get a community customized peripheral gift package (data line+ Mouse Pad + mascot)

Reward Distribution Method:

· The task is confirmed completed upon email submission. The e-mail should be sent to the official mailbox of the community:

o Official email address of OpenMLDB community:

o Mail naming: OCP phase I + task code (e.g. 826)

o The email content shall contain the following valid information: GitHub task link + GitHub ID + a valid recipient information (recipient name / mobile phone number / recipient address)

· After verifying the completion of the task, the contributor reward (customized peripheral gift package by the developer community) will be sent by courier

Task Description:

· Submission Form: all contributor tasks are submitted on GitHub platform in the form of pull request

· Reward criteria: submission time and task completion quality, and the selection rules are determined by the OpenMLDB community

· Deadline of the first contributor’s task: February 6, 2022

· Exchange period: please take the initiative to provide relevant information to the official email after the task is completed , late submission will be deemed as forfeit

(* the OpenMLDB community reserves the right of final interpretation of the activity)

Task Reference Link

OpenMLDB SQL Built-in Function Development Guide ​

OpenMLDB Contribution Guide​

“OpenMLDB Developer Group and, Rights and Obligations”

Let’s become an OpenMLDB contributors together and we look forward for you to join us!




OpenMLDB is an open-source machine learning database that provides a full-stack FeatureOps solution for production.

Love podcasts or audiobooks? Learn on the go with our new app.

Recommended from Medium

How to Build an Enterprise SaaS MVP

CS373 Spring 2021 Blog #3, Saumyaa Krishnan

Cisco tutorial: How to optimize HSRP

A Snyk’s Post-Mortem of the Malicious event-stream npm package backdoor

C Pointer? What is it for?

Debugging Local Mobile Pages on Android Phone Using Chrome Developer Tools

Daily’s 2019 — Thank You All!

Ambassador 1.7.3 security release

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store
OpenMLDB Blogs

OpenMLDB Blogs

OpenMLDB is an open-source machine learning database that provides a full-stack FeatureOps solution for production.

More from Medium

UML Model Creation from Requirements using Domain Ontology

Spark-NLP for Healthcare in AWS EMR

Deploying Conduit on Heroku

Tutorial: Compare JSON documents and apply patches in TerminusDB and MongoDB

JSON Diff and Patch