Developing AI-Enabled Systems 7.5 credits

This course introduces 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. It also includes 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 AI/ML-enabled 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