The New DBfication of ML/AI with Arun Kumar - #553
Analysis
This podcast episode from Practical AI discusses the "database-ification" of machine learning, a concept explored by Arun Kumar at UC San Diego. The episode delves into the merging of ML and database fields, highlighting potential benefits for the end-to-end ML workflow. It also touches upon tools developed by Kumar's team, such as Cerebro for reproducible model selection and SortingHat for automating data preparation. The conversation provides insights into the future of machine learning platforms and MLOps, emphasizing the importance of tools that streamline the ML process.
Key Takeaways
Reference
“We discuss the relationship between the ML and database fields and how the merging of the two could have positive outcomes for the end-to-end ML workflow.”