Page 38 - Crowdsourcing AI and Machine Learning solutions for SDGs - ITU AI/ML Challenges 2024 Report

Crowdsourcing AI and Machine Learning solutions for SDGs

13 Resources

The following resources will be available to the participants of the ITU AI/ML Challenge:
• Mentors: Experts mentoring students to enhance their skills and understanding of AI/ML
• Note: "Mentors" may mentor students’ participation in the "students track" or sponsor-
nominated students and professionals. The mentors are active throughout the challenge.
• ITU Server with on-prem GPUs and orchestration software provided by JarvisAI
• Networking platform of experts in AI (“Neural Network”);
• Standards and pre-standards activities related to AI and machine learning.
• Zindi: An African start-up hosting some of the problem statements of the ITU AI/ML
Challenge since 2023
• AIIA – challenge management software hosting problem statements of the ITU AI/ML in
the 5G Challenge
• Software: Adlik, ONAP, O-RAN OSC Resources, Acumos
• Cloud Credits (based on partner support)
• Toolsets and APIs from partners (set by sponsors)
• ITU AI/ML Challenge website
• Datasets:

o hosted on contest platforms: provided by sponsors, partners and collaborators
o open datasets from e.g. Kaggle, AIcrowd, OpenML
o Simulated datasets from collaborators

Compute platform

ITU provides a state-of-the-art, free-of-charge computing platform to participants of the
Challenge who do not have adequate access to compute in their respective institutions. The
computing platform will provide participants with access to:
• Free GPUs and CPUs
• Hosted Jupyter notebook server
• Python kernel
• Pre-installed machine learning packages, e.g. PyTorch and Tensorflow

In some of the problem statements, a baseline or reference solution may be offered which may
include implementations using Jupyter notebooks.

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