Winners

CONGRATULATION TO THE WINNERS!

1stextNPH
Nguyễn Trọng Tuấn, Nguyễn Xuân Trường, Nguyễn Sỹ Mạnh, Nguyễn Tiến Dũng, Nguyễn Công Đoàn
1stMiracle
Lương Quang Dũng, Dương Ngọc Thiện, Trần Phan Quốc Đạt
1stBáo con
Vũ Đức Cương, Nguyễn Trường Thành, Khuất Nguyên Cương, Dương Đức Duy, Vũ Minh Vương
1stmomentum
Phạm Thái Hoàng Tùng, Phạm Minh Khiêm, Nguyễn Hoàng Minh, Nguyễn Quốc Sinh, Vũ Trọng Đức
Leaderboard
Phase 1
Phase 2
Phase 3
Prize
1
extNPH0.94
Prize
2
The man0.94
Prize
3
Miracle0.92
Prize
4
CungDaLauRoi0.91
Prize
5
UIT_Deadline0.90
Prize
6
RedRock0.90
Prize
7
Zephyrus0.90
Prize
8
Báo con0.89
Prize
9
Top Gun0.89
Prize
10
MCBK0.88
Prizes
2nd
10.000.000 VND
& In-kind
presents
1st
100.000.000 VND
& In-kind
presents
3rd
3.000.000 VND
& In-kind
presents
Values
  • Solve real world problems faced by real businesses
  • Hosted and organized by experts from big tech companies
  • Skills gained by this competition will be applied in any AI projects
Challenges

MLOps Marathon 2023 consists of three phases: Warm Up, Speed Up, and Finish Up, each with two stages. While all phases involve ML problems, they will vary in difficulty levels and scoring weights.

In stage 1 of each phase, teams will be assigned several ML problems and must train models to solve them. Following this, teams will deploy APIs on designated remote servers to serve the ML models.

In stage 2, the organizer will send batch requests to teams' APIs, and the APIs must return predictions for the data included in the batch requests. Results will be evaluated using both ML metrics, such as AUC and F1, as well as system metrics, such as response time.

Each phase will have different levels of challenge in the data sent in batch requests. This may include bad data such as outliers, unknown formats, or missing values, as well as drifted data, such as data or concept drift. Teams will be expected to continuously improve their models based on the evaluation results.

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Timeline
Stage #1
The first 3-4 weeks
  • Given several ML problems
  • Teams train corresponding models
  • Teams deploy Online serving APIs on given remote servers
Stage #2
The last 3 days
  • The organizer sends batch requests to teams’ APIs
  • APIs’ responses are evaluated
  • Teams continuously improve models based on evaluation results
Phase #1: Warm Up
  • Familiarize teams with challenge structure
  • 4 weeks (5/6 - 3/7)
  • 5% total score
Phase #2: Speed Up
  • Challenge teams with bad data
  • 4 weeks: 3/7 - 31/7
  • 25% total score
Phase #3: Finish Up
  • Challenge teams with higher levels of bad data
  • 4 weeks: 31/7 - 28/8
  • 70% total score
Organizers
Dr. Long Pham
Dr. Long Pham
Senior Research Coordinator
UCC, Ireland
Dr. Harry Nguyen
Dr. Harry Nguyen
Assistant Professor
UCC, Ireland
Dr. Sonny Vu
Dr. Sonny Vu
Senior AI/ML Lead
Devr INC, Sweden
Nghia Mai
Nghia Mai
Marketing Consultant
Women in AI, Ireland
Thuy Trinh Nguyen
Thuy Trinh Nguyen
Research Assistant
University of New South Wales
Nguyen Dang
Nguyen Dang
AI Engineer
FTECH, Vietnam
Quan Dang
Quan Dang
Senior Data Scientist
Yokogawa, Singapore
Tung Dao
Tung Dao
Senior ML Engineer
Shopee, Singapore
Committee Members
Duc Tan
Duc Tan
Data Engineer
Neurond
Duy Kha
Duy Kha
AI Engineer
NamiQ
Hoang Ngoc Loc
Hoang Ngoc Loc
Alumnus
Danang University Technology
Ngoc Viet Tien
Ngoc Viet Tien
Lead Technical Architecture
Ginno
Partners
Diamond Sponsors
/sponsors/AI_Center_FSOFT.png/sponsors/TCB_02.png
Gold Sponsors
/sponsors/CMC_Telecom 2019_03.png/sponsors/AWS_logo_RGB.png
Silver Sponsors
/sponsors/dopikai.png/sponsors/sotatek.png/sponsors/imba.png
Communication Partners
/sponsors/KHPT_2023.jpg
Frequently Asked Questions
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The MLOps competition is a way for developers, engineers and enthusiasts to explore MLOps in a real-world setting. Participants will be able to develop, deploy, and scale their ML model in order to compete for prizes.
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Resources such as training materials, tutorials, and sample projects. Additionally, access to a cloud-based platform will be available in order to build, deploy and monitor their ML models.
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Technologies allowed for use in the MLOps competition include any open source or commercially available MLOps tools, libraries, frameworks, or programming languages.
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There are many resources available for learning MLOps, including online courses, tutorials, and books. Some popular online platforms that offer MLOps courses include Coursera, Udacity, and edX. Additionally, the necessary skills for MLOps Marathon 2023 have been distilled and designed by the MLOpsVN team into a course called MLOps Crash Course. Since 12 Dec 2022, the course has been offered free of charge to the community. You can register the course here with Access Code "MLOPS-MARA".

About us

Open Factor Foundation is the community organization behind AIHUB.ML and MLOpsVN. Over the past years, AIHUB.ML has organized many AI-related competitions, generating great interest and impact in the tech and business communities such as AICOVIDVN 115M, MC-OCR 2021, and VLSP 2021, and now it’s MLOps Marathon.

Through these competitions, we commit to enable Vietnamese youth to bring inclusive and effective AI solutions to industries and sectors. We empower the youth with systematic and practical best practices from global AI industry tailored for Vietnam market.