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26D111SIVI - Software Engineering for AI-enabled Systems

Course specification
Course title Software Engineering for AI-enabled Systems
Acronym 26D111SIVI
Study programme Electrical Engineering and Computing
Module Software Engineering
Type of study doctoral studies
Lecturer (for classes)
Lecturer/Associate (for practice)
    Lecturer/Associate (for OTC)
      ESPB 9.0 Status elective
      Condition
      The goal The goal of the course is to provide students with the knowledge and skills necessary to move from developing machine learning models to building, implementing, maintaining, and managing production AI systems. To enable students to understand and apply the engineering, architectural, testing, operational, and ethical aspects of creating products with machine learning components.
      The outcome Upon completion of the course, the student should be able to: analyze design trade-offs – not only model accuracy, but also latency, operational costs, scalability, explainability, and security; plan and implement systems that are robust to model errors, have testing, monitoring, and can be maintained in production; design infrastructure for data and models; evaluate system quality.
      Contents
      Contents of lectures • Model and system quality: latency, scalability, operational costs, explainability, bias, privacy, security. • Infrastructure & MLOps: CI/CD for ML, model and data versioning, production experimentation, drift and feedback loop detection. • Teams and processes: roles of software engineers, data scientists, operations, domain experts; collaboration, responsibilities. • Ethics and accountability.
      Contents of exercises • Programming assignments that include building a modeling pipeline, implementing services, automating testing and deployment. • A project where students design, implement, deploy, and monitor a system that uses a machine learning model in production, and manage its quality, scaling, monitoring, and errors. • Study papers aimed at understanding theoretical aspects and studying practical cases.
      Literature
      1. C. Huyen, Designing Machine Learning Systems, O'Reilly 2022. (Original title)
      2. N. Gift, A. Deza, Practical MLOps: Operationalizing Machine Learning Models, O'Reilly 2021. (Original title)
      3. M. Staron, Machine Learning Infrastructure and Best Practices for Software Engineers, Packt 2024. (Original title)
      4. S. Russell, P. Norvig, Artificial Intelligence: A Modern Approach, 4th ed, Pearson 2021. (Original title)
      5. Selected research papers
      Number of hours per week during the semester/trimester/year
      Lectures Exercises OTC Study and Research Other classes
      8
      Methods of teaching Mentoring, individual work on the project.
      Knowledge score (maximum points 100)
      Pre obligations Points Final exam Points
      Activites during lectures Test paper
      Practical lessons Oral examination 30
      Projects 70
      Colloquia
      Seminars