MLOps Without Magic

This post provides a detailed guide on implementing intermediate MLOps using simple Python code, without relying on specific MLOps frameworks like MLflow or DVC. Key sections include setting up a project structure with designated folders for data, models, and results, using command line tools for preprocessing, training, and predicting, and managing experiments using a script called tasks.py. The guide emphasizes simplicity, maintainability, and effectiveness, suitable for both local and cloud-based workflows.