Developing AI-Enabled Systems 7.5 credits

This course introduces students to the principles and practices of software engineering with a focus on AI/ML-enabled systems. It covers the lifecycle of AI systems—from data collection and model development to continuous integration. We also do a basic overview of deployment and monitoring—while emphasizing the unique challenges that arise in AI system development. Students will gain hands-on experience on using the common technologies (such as for example Python, Pandas and Git) to build, manage, and maintain AI/ML-enabled systems. By the end of the course, students will be equipped to apply software engineering practices to AI projects and ensure the quality, security, and scalability of their systems. The course includes the following elements, all within the context of AI/ML-enabled systems: - Software engineering practices and processes - Design decisions - Version control and collaboration with version control systems (e.g.: Git). - Testing and continuous integration (CI) - Integrating AI/ML model into a larger system - Managing dependencies and environments