AI Systems in Production 7.5 credits

This 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