Software Engineering für Bilderkennungs ML Pipeline
Wann und Wo
Semesterprojekt: Dienstag, 13-17 Uhr, RUD 26, 1'305
Wer
Dozent: Eik Reichmann / Marc Carwehl
Beschreibung und Aufbau der Lehrveranstaltung
Due to the recent improvements in AI algorithms and the increase in compute power, the demand and adoption of AI-based functionalities has dramatically increased. However, developing AI-based systems is not only about the best possible algorithm but more about a reliable training and testing pipeline that process data, trains models, and evaluates results in a reproducible way.
This semester project focuses on the practical aspects of engineering AI systems. Students will implement a complete pipeline, including data gathering and preprocessing as well as model training and evaluation. The task is image recognition. The students are encouraged to apply software engineering practices such as modular design, version control, reproducibility, parallel training and testing to their ML pipeline. To introduce some additional motivation students will compete against each other in the development of the best model.
In the end they will have to write a short report about their approach.
Organisatorisches
Die Lehrveranstaltung findet auf Englisch statt.