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 scenario
The fintech (~140 staff) currently pays ~USD 18k/month to a vendor classification API; bringing classification in-house would clear regulatory pressure on data residency.
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.
Large Language Models
Master · Generative Ai
Strong alignment
This challenge maps to Large Language Models 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.
- Lora Fine Tuning
Apply lora fine tuning to solve real industry problems and demonstrate production-level capability.
- Open Weight Llms
Apply open weight llms to solve real industry problems and demonstrate production-level capability.
- Classification
Apply classification to solve real industry problems and demonstrate production-level capability.
- Llm Evaluation
Apply llm evaluation to solve real industry problems and demonstrate production-level capability.
- Inference Benchmarking
Apply inference benchmarking to solve real industry problems and demonstrate production-level capability.
- Deployment Design
Apply deployment 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:
Machine 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