📅 2025-10-05 — Session: Revamped ML Project Structure and Data Pipeline
🕒 01:25–02:45
🏷️ Labels: Machine Learning, Project Management, Refactoring, Data Pipeline
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The primary objective of this session was to revamp the structure of a machine learning project, focusing on modularization, data pipeline improvements, and configuration management.
Key Activities
- Structured Revamp Plan: Developed a comprehensive plan to revamp the ML project, including repository layout, environment setup, and migration steps.
- Module Refactoring: Detailed refactoring of the EPH project scripts into a modular structure to prevent target leakage and ensure proper cross-validation.
- Production-Ready IO Module: Created a robust Python module for data handling tasks, focusing on directory management and file operations.
- YAML Configuration Loader: Developed a minimal script for loading YAML configurations, ensuring safe handling of paths and defaults.
- Data Pipeline Structure: Outlined responsibilities and conceptual layers for preprocessing data, separating universal alignment from project-specific transformations.
- Clarification on CPython Artifacts: Provided guidance on handling CPython internal artifacts and future imports to avoid coding pitfalls.
- Training Loop Analysis: Critiqued a training loop for a classifier and regressor, identifying issues such as data leakage and recommending best practices.
Achievements
- Successfully outlined a modular structure for the ML project and data pipeline.
- Created efficient and minimal scripts for configuration and data handling.
- Addressed and clarified common pitfalls in Python coding practices, particularly with future imports.
- Provided actionable recommendations for improving training loops and production pipelines.
Pending Tasks
- Implement the proposed refactoring and modularization in the EPH project.
- Apply the recommended training loop fixes and production pipeline improvements.