Fine-Tune a 3B Open-Weight Model for Customer Support Triage
Overview
What this challenge is about.
Fine-tune a 3B open-weight model with LoRA on 40,000 fintech support tickets, evaluate F1 and latency, and get a verifiable certificate.
The Brief
What you'll do, and what you'll demonstrate.
Replace a vendor classification API with a fine-tuned open-weight 3B model that beats it on quality, cost, or both — with a fallback plan.
Earning criteria — what you'll demonstrate
- Apply LoRA fine-tuning to a 3B open-weight model on a real classification task
- Benchmark a fine-tuned model against a vendor API on quality, latency, and cost
- Design a deployment with a fallback path and basic monitoring
- Reason about data-residency benefits of in-house LLMs
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
Careers
Roles this prepares you for.
Real titles. Real skill bridges. Pick the one closest to your trajectory.
Career paths this builds toward
Canonical rolesMachine Learning Engineer
Owning a LoRA fine-tune from data to deployment recommendation is core MLE work at any AI-forward company moving off vendor APIs.
This challenge sharpens
- lora-fine-tuning
- classification
- deployment-design
AI Engineer
Wiring an open-weight model into a production-shaped service with monitoring and fallback is the AI-engineer skillset that scaling teams hire for.
This challenge sharpens
- open-weight-llms
- inference-benchmarking
- deployment-design
MLOps Engineer
The cost/latency benchmark plus the fallback design bridges directly into MLOps work on serving platforms.
This challenge sharpens
- inference-benchmarking
- deployment-design
- llm-evaluation