Explore the intersection of machine learning/artificial intelligence and database technologies in this 57-minute guest lecture by Dr. Arun Kumar from the University of California, San Diego. Delve into the challenges of scalability, usability, and manageability in ML/AI applications, and discover how database principles can help democratize these technologies. Learn about query optimization for ML systems and benchmarking data preparation in AutoML platforms through case studies. Gain insights into bridging the gap between research and practice in ML/AI, and understand the importance of community mechanisms in fostering interdisciplinary collaboration. Covers topics such as the golden age of ML/AI, platforming ML, project Cerebro, model selection, data preparation, schema inference, and the "DBification" of ML.
Overview
Syllabus
Introduction
Golden Age of MLAI
democratize MLAI
my work
Outline
ML Concerns
Platforming ML
Challenges
My Research
Running Deep Learning
Project Cerebro
Model Selection
Data Size
Model Hopper
Cerebro adoption
Data preparation
Schema Inference
Type Inference
The Dubification of ML
Challenges in ML
How to do ML research
Courses
Conferences
Collaboration
Conclusion
Wrap up
Taught by
The University of Melbourne
