Domain-Adapt an NLP Pipeline from News to Customer-Support Tickets
Overview
What this challenge is about.
Domain-adapt an NLP pipeline from news to support tickets in PT-BR and ES, then compare performance to earn your verifiable certificate.
The scenario
The startup (around 75 staff, around USD 9M ARR, around 4M monthly active users across LatAm) sees support-ticket understanding as the bottleneck for its planned support-assistant 2.0 launch.
The Brief
What you'll do, and what you'll demonstrate.
Quantify how much continued pretraining + fine-tuning beats fine-tuning alone for NER + intent on multilingual customer-support tickets.
Earning criteria — what you'll demonstrate
- Apply continued pretraining of a multilingual encoder on a domain corpus
- Fine-tune downstream NLP tasks and compare against meaningful baselines
- Reason about positive and negative effects of domain adaptation
- Communicate domain-adaptation cost vs. benefit to a product-NLP audience
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Aligned coursework coming soon.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Transfer Learning
Apply transfer learning to solve real industry problems and demonstrate production-level capability.
- Domain Adaptation
Apply domain adaptation to solve real industry problems and demonstrate production-level capability.
- Continued Pretraining
Apply continued pretraining to solve real industry problems and demonstrate production-level capability.
- Transformer
Apply transformer to solve real industry problems and demonstrate production-level capability.
- Multilingual Evaluation
Apply multilingual evaluation to solve real industry problems and demonstrate production-level capability.
- Pytorch
Apply pytorch 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:
NLP Engineer
Continued-pretraining + fine-tuning loops on multilingual encoders are the NLP-engineer's signature work at any multilingual consumer-AI product.
This challenge sharpens
- transformer
- domain-adaptation
- multilingual-evaluation
Applied AI Scientist
Honest comparison of domain-adaptation strategies is exactly the applied-AI-scientist's craft when justifying compute spend to leadership.
This challenge sharpens
- transfer-learning
- continued-pretraining
- domain-adaptation
Machine Learning Engineer
Shipping a reproducible adaptation + fine-tune pipeline that engineering can re-run on the next ticket batch is core MLE territory.
This challenge sharpens
- pytorch
- transformer
- transfer-learning