mlscaffold 0.2.1

Create a ready-to-use ML project structure with one command.

🚀 mlscaffold

mlscaffold is a Python CLI tool to bootstrap Machine Learning projects quickly.
It creates a clean folder structure, boilerplate files, and an ML workflow checklist, so you can start coding immediately.

Think of it as create-react-app — but for ML projects.

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✨ Features

• 📂 Automatically generates a standard ML project structure
• 📝 Includes ML_Workflow.txt for step-by-step guidance
• ⚡ Boilerplate folders and files:
  • src/ → Python source code (main.py, __init__.py)
  • data/raw & data/processed → Data storage
  • models/ → Trained models
  • notebooks/ → Jupyter notebooks
  • docs/ → Project documentation
  • tests/ → Unit or smoke tests
  • requirements.txt → Python dependencies
  • .gitignore → Recommended ignores
• 🧑‍💻 Easy to use and extend
• 🔄 Works on Windows, Linux, and Mac

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📦 Installation

pip install mlscaffold

🚀 Usage

Create a new ML project:

mlscaffold my-ml-project

output

✅ ML project 'my-ml-project' created at: /your/path/my-ml-project
👉 Next : cd my-ml-project

📁 Project Structures

mlscaffold supports three project types: basic, research, and production. Choose the one that fits your workflow:

1. Basic

For quick experiments, prototypes, or simple scripts.

my-ml-project/
├─ src/
│  ├─ __init__.py
│  └─ main.py
├─ data/
├─ models/
├─ notebooks/
├─ tests/
│  └─ test_smoke.py
├─ ML_Workflow.txt
├─ requirements.txt
├─ README.md
└─ .gitignore

2. Research

For academic, research, or more complex projects with experiments and documentation.

my-ml-project/
├─ src/
│  ├─ __init__.py
│  └─ main.py
├─ data/
│  ├─ raw/
│  └─ processed/
├─ models/
├─ notebooks/
├─ docs/
├─ experiments/
├─ tests/
│  └─ test_smoke.py
├─ ML_Workflow.txt
├─ requirements.txt
├─ README.md
└─ .gitignore

3. Production

For production-ready ML systems, APIs, and CI/CD integration.

my-ml-project/
├─ src/
│  ├─ __init__.py
│  └─ main.py
├─ data/
├─ models/
├─ notebooks/
├─ docs/
├─ api/
├─ tests/
│  └─ test_smoke.py
├─ .github/
│  └─ workflows/
│      └─ ci.yml
├─ Dockerfile
├─ ML_Workflow.txt
├─ requirements.txt
├─ README.md
└─ .gitignore

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ML_Workflow.txt includes the full ML workflow checklist:

0) Project setup
1) Problem framing
2) Data collection
3) Preprocessing
4) Exploratory Data Analysis (EDA)
5) Baseline & Models
6) Training & Evaluation
7) Hyperparameter Tuning
8) Packaging & Artifacts
9) Deployment
10) Monitoring & Iteration

🤝 Contributions

We welcome contributions! Please read CONTRIBUTIONS.md