Machine Learning in Production | How to write Modular Code скачать в хорошем качестве

Machine Learning in Production | How to write Modular Code

Description: Monolithic scripts might seem fine for quick experiments, but they quickly become a nightmare in real-world machine learning projects. In this video, I break down the pitfalls of cramming your entire ML workflow into a single file from data loading to deployment and show you how to transform it into a clean, modular, and scalable structure. What’s inside: ✅ Why monolithic scripts are bad for scaling, debugging, and collaboration ✅ Debugging challenges (“walls of text”) and how to fix them ✅ Modular ML code design with practical Iris dataset implementation ✅ Recommended folder structures for maintainable ML projects ✅ Using YAML configuration files for flexible experiments ✅ Test-Driven Development (TDD) applied to ML Bonus: Watch me restructure the Iris dataset project step-by-step into a modular ML pipeline that’s ready for collaboration and production. Notes link:- https://miro.com/app/board/uXjVJXhoS9...