April 30, 2024Make your MLOps code base SOLID with Pydantic and Python’s ABC
MLOps projects are straightforward to initiate, but challenging to perfect. While AI/ML projects often start with a notebook for prototyping, deploying them directly in production is often considered poor practice by the MLOps community . Transitioning to a dedicated Python code base is essential for industrializing the project, yet this move presents several challenges: 1) How can we maintain a code base that is robust yet flexible for agile development? 2) Is it feasible to implement proven design patterns while keeping the code base accessible to all developers? 3) How can we leverage Python’s dynamic nature while adopting strong typing practices akin to static languages? Throughout my career, I have thoroughly explored various strategies to make my code base both simple and powerful.
