Overview
What this challenge is about.
Optimize NCCL, batch size, and workers for a 7B fine-tune, then deliver a one-page recipe and Python helper. Earn a verifiable certificate.
The scenario
The Singapore startup (around 35 staff, post-Series A) burns around USD 90k/month on rented compute and treats a 20% throughput improvement as a meaningful runway extension.
The Brief
What you'll do, and what you'll demonstrate.
Find the highest-impact knobs for distributed-training throughput on the cluster and ship a recipe + helper script the team applies on day one.
Earning criteria — what you'll demonstrate
- Define a meaningful search space for distributed-training knobs
- Run a budget-constrained hyperparameter search at cluster scale
- Quantify the marginal impact of each knob honestly
- Package systems knowledge as a reusable team tool
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Machine Learning Systems
Master · Ai Systems
Strong alignment
This challenge maps to Machine Learning Systems at the Master level. It sharpens the same practical skills your coursework expects — but in a real industry context with actual constraints and deliverables.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Distributed Training
Apply distributed training to solve real industry problems and demonstrate production-level capability.
- Hyperparameter Tuning
Apply hyperparameter tuning to solve real industry problems and demonstrate production-level capability.
- Nccl
Apply nccl to solve real industry problems and demonstrate production-level capability.
- Pytorch
Apply pytorch to solve real industry problems and demonstrate production-level capability.
- Throughput Modeling
Apply throughput modeling to solve real industry problems and demonstrate production-level capability.
- Experiment Design
Apply experiment design to solve real industry problems and demonstrate production-level capability.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
MLOps Engineer
Tuning distributed-training systems for throughput and shipping a reusable recipe is the work that platform MLOps engineers do on training infrastructure teams.
This challenge sharpens
- distributed-training
- nccl
- throughput-modeling
Machine Learning Engineer
Hands-on knowledge of NCCL, dataloader, and gradient-accumulation tuning is the systems-MLE skill set that startups training their own models hire for.
This challenge sharpens
- distributed-training
- pytorch
- hyperparameter-tuning
AI Solutions Architect
Translating cluster-tuning wins into runway extension and a deployable recipe is core AI solutions architecture for cloud providers and consulting firms.
This challenge sharpens
- throughput-modeling
- distributed-training
- experiment-design