AI in research software: Best practices for developing and using ML modelsCompact course: AI in research software: Best practices for developing and using ML models


  • Date: 7.6.24 9am-1pm
  • Instructor: Dr. Inga Ulusoy
  • Venue: Mathematikon Bauteil A, INF 205, 5/104

This is a half day course.


Basic Python knowledge and knowledge about data processing, ML models and training of models is required. 


The AI revolution is moving even more rapidly than the digital revolution and leads to the emergence of completely new tools and technologies that affect the scientific process. In this course, we will learn about data-based research software, tools and communities that are relevant in creating and sharing such software, and about best practices in training machine-learning models. Research software that is based on ML models requires an additional layer of best practices in the implementation, including testing of non-deterministic processes. Security aspects as well as bad examples are discussed to highlight the importance of adhering to a best practices code of conduct.

Learning Objectives

After the course participants will be able to

  • Understand and follow best practices in the underlying dataset
  • Understand and follow best practices in training ML models
  • Write better data-based research software, including appropriate tests
  • Avoid negative impact from legal and security issues


There is an instance of this course scheduled: Signup for the next scheduled course