Data ml recommended
ML Project Structure
Machine learning project structure patterns covering directory organization, experiment tracking, reproducibility, data versioning, and MLOps-ready architecture.
- Difficulty
- intermediate
- Read time
- 1 min read
- Version
- v1.0.0
- Confidence
- established
- Last updated
Quick Reference
ML Project: Cookiecutter data science structure. Separate data/models/notebooks/src. DVC for data versioning. MLflow/W&B for experiment tracking. requirements.txt or pyproject.toml for dependencies. Config files (YAML) for hyperparameters. Makefile for common tasks. Git for code, DVC for large files. Reproducible environments.
Use When
- Machine learning projects
- Data science experiments
- Model training pipelines
- MLOps setup
Skip When
- Simple data analysis (use notebooks)
- Production inference only
- Pre-built ML services
ML Project Structure
Machine learning project structure patterns covering directory organization, experiment tracking, reproducibility, data versioning, and MLOps-ready architecture.