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A production-ready CLI tool that scaffolds AI/ML projects interactively

Project description

๐Ÿš€ mzl-init

A production-ready AI/ML project scaffolding CLI โ€” like Vite, but for Machine Learning.

Python License: MIT

Installation

pip install -e .

Usage

Interactive mode (recommended)

mzl-init my_project

Launches an arrow-key prompt to select:

  • Project type (Tabular ML / Deep Learning / NLP / Computer Vision / LLM App)
  • Framework (PyTorch / TensorFlow / Scikit-learn)
  • Optional features: FastAPI, Docker, MLflow, DVC, Hydra

Non-interactive mode (CI/CD)

mzl-init init my_project --type tabular --framework sklearn --docker --mlflow

Check your environment

mzl-init doctor

Version

mzl-init --version

Generated Structure

my_project/
โ”œโ”€โ”€ configs/            # YAML configuration files
โ”œโ”€โ”€ data/raw|processed|external
โ”œโ”€โ”€ docs/
โ”œโ”€โ”€ models/             # Trained artifacts
โ”œโ”€โ”€ notebooks/
โ”œโ”€โ”€ reports/
โ”œโ”€โ”€ scripts/
โ”œโ”€โ”€ src/
โ”‚   โ”œโ”€โ”€ data/loader.py
โ”‚   โ”œโ”€โ”€ features/build_features.py
โ”‚   โ”œโ”€โ”€ models/model.py         โ† framework-specific
โ”‚   โ”œโ”€โ”€ training/train.py       โ† MLflow-aware
โ”‚   โ”œโ”€โ”€ evaluation/evaluate.py
โ”‚   โ”œโ”€โ”€ inference/predict.py
โ”‚   โ””โ”€โ”€ utils/logger.py
โ”œโ”€โ”€ tests/
โ”œโ”€โ”€ deployment/api|docker|cloud
โ”œโ”€โ”€ .env
โ”œโ”€โ”€ .gitignore
โ”œโ”€โ”€ main.py
โ”œโ”€โ”€ requirements.txt
โ””โ”€โ”€ pyproject.toml

License

MIT

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