NLP in Industry: challenges and best practices
Module Description
| Course | Module Abbreviation | Credit Points |
|---|---|---|
| BA-2010[100%|75%] | CS-CL | 6 LP |
| BA-2010[50%] | BS-CL | 6 LP |
| BA-2010[25%] | BS-AC | 4 LP |
| BA-2010 | AS-CL | 8 LP |
| Master | SS-CL-TAC | 8 LP |
| Lecturer | Daniel Dahlmeier |
| Module Type | |
| Language | English |
| First Session | 27.07.2026 |
| Last Session | 31.07.2026 |
| Time and Place | tba. |
| Commitment Period | tbd. |
Participants
All advanced CL Bachelor students and all CL master students. Students from MSc Data and Computer Science or MSc Scientific Computing with Field of Application Computational Linguistics are welcome after getting permission from the lecturer. MSc Scientific Computing students can only take the course as HS for 8 LP. If the seminar should be oversubscribed, CL students will have priority.
Prerequisite for Participation
Assessment
Content
This seminar explores the challenges and best practices for natural language processing (NLP) in industry with a focus on deep learning and large language models (LLMs). The course will focus on practical topics, including data engineering, prompt engineering, AI architecture, finetuning, model deployment, evaluation, and real-world considerations, such as legal/data protection, viable business cases, and ethical implications. Students will engage with key concepts from the textbooks Designing Machine Learning Systems and AI Engineering by Chip Huyen and will be presenting selected chapters in class. Each student will present a selected topic and submit a written report.
Additionally, participants will implement a course project to gain practical insights into building modern LLM systems. For questions about the seminar, please email x0unj76@uni-heidelberg.de.
Literature
- AI Engineering. Chip Huyen. 2025.
- Designing Machine Learning Systems. Chip Huyen. 2022.
