Programme Outlines and Overviews

AI Systems in Production 7.5 credits

Course content

The course explores the challenges and solutions involved in deploying, managing, and maintaining AI/ML-enabled systems in production environments. It covers architectural design, deployment pipelines, data versioning, configuration management, and monitoring strategies to ensure scalability, reliability, and performance of AI systems. Students will gain hands-on experience using appropriate languages and tools, such as Python, Git, MLFlow, Prometheus, and Databricks, to develop, deploy, and monitor AI systems in real-world scenarios. The course includes the following elements: - Architecture and Design for AI-Enabled Systems in Production - Deployment Pipelines and Model Lifecycle Management - Data Versioning and Configuration Management - Quality Attributes and Operational Challenges - Monitoring, Logging, and Error Management

Entry requirements

Passed courses at least 90 credits within the major subject computer engineering, computer science, informatics, information systems or information technology, including a minimum of 15 credits in mathematics and at least 30 credits in programming/software development, or alternatively passed courses at least 150 credits from the programme Computer Science and Engineering, and taken Developing AI-enabled Systems, 7.5 credits and Data Science, 7.5 credits.

Level: Second cycle

Course/Ladok-code: T2AIPN

School: School of Engineering