Multilingual NLP, Production, Ethics and Capstone

Why this module belongs in the course

Production quality means known limits, measurable behaviour, safe data handling, monitoring and a rollback path. Deployment is the start of observation, not the end of a project.

Before you begin

The concepts and project evidence from Module 15. You should also be able to create a Python virtual environment and keep private or employer data out of the exercise.

Four lessons, one connected result

1. Lesson 1Multilingual and Low-Resource NLP with Urdu and Code-Switching95 min · Advanced
2. Lesson 2Production NLP APIs, Batching, Caching and Drift Monitoring100 min · Advanced
3. Lesson 3Responsible NLP: Privacy, Bias, Copyright and Documentation90 min · Advanced
4. Lesson 4Capstone: Build a Multilingual Knowledge Discovery System180 min · Advanced

How to know you are ready to continue

Complete the checkpoint without copying the worked example. Keep the code, output and a short decision note. Your note should explain one choice, one failure you observed and one limitation a reviewer should know.

Primary references for this module

The lessons explain the ideas in original wording. Use these primary or official sources when a library interface, standard or research claim needs verification.

• https://arxiv.org/abs/1911.02116
• https://universaldependencies.org/ur/
• https://huggingface.co/docs/tokenizers/
• https://huggingface.co/docs/hub/datasets-cards
• https://fastapi.tiangolo.com/
• https://prometheus.io/docs/practices/instrumentation/
• https://docs.python.org/3/howto/logging.html
• https://huggingface.co/docs/hub/model-cards