Building an ML Platform from scratch

The podcast explores the challenges and potential of building MLOps systems, particularly focusing on the use of SQL Mesh, DuckDB, Prefect, and GitHub for managing data transformations and machine learning workflows. The hosts debug issues encountered while connecting models in SQL Mesh, emphasizing the importance of linting and documentation to avoid common errors related to SQL syntax and time zone handling. They discuss the benefits of SQL Mesh's state management and virtual environments for streamlining data pipelines and preventing redundant queries. The conversation shifts to feature engineering, debating the merits of monolithic versus decoupled pipelines and the role of feature stores in promoting reusability and collaboration among data scientists. The hosts also touch on the balance between rapid iteration and code quality, highlighting the need for testing and validation in complex systems.