The New DBification of ML-AI

This course aims to explore the intersection of machine learning/artificial intelligence (ML/AI) and databases to address scalability, usability, and manageability concerns in ML/AI applications. The learning outcomes include understanding the importance of bridging the gap between ML/AI and database principles, techniques, and tools, as well as exploring case studies on query optimization for ML systems and benchmarking data preparation in AutoML platforms. The course teaches skills such as query optimization, benchmarking, data preparation, schema inference, and type inference. The teaching method involves a lecture format with case studies and practical examples. The intended audience for this course includes data scientists, machine learning engineers, AI researchers, and professionals interested in the intersection of ML/AI and database technologies.