CS5720 - Best Practices for Code Organization

Core Principles

🎯

Separation of Concerns

Each module should have a single, well-defined responsibility
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Modularity

Break code into reusable, independent components
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Consistency

Follow consistent naming conventions and code style
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Maintainability

Write code that's easy to understand and modify

ML Project Structure

📁 ml-project/ ├── 📁 src/ │ ├── 📁 data/ │ │ ├── 📄 __init__.py │ │ ├── 📄 load_data.py │ │ └── 📄 preprocess.py │ ├── 📁 models/ │ │ ├── 📄 __init__.py │ │ ├── 📄 base_model.py │ │ └── 📄 neural_net.py │ ├── 📁 training/ │ │ ├── 📄 __init__.py │ │ ├── 📄 trainer.py │ │ └── 📄 metrics.py │ └── 📁 utils/ │ ├── 📄 __init__.py │ └── 📄 helpers.py ├── 📁 configs/ │ ├── ⚙️ model_config.yaml │ └── ⚙️ data_config.yaml ├── 📁 tests/ └── 📄 requirements.txt

💡 Click the structure above

Explore detailed explanations of each directory and its purpose in ML projects.

Essential ML Code Organization Practices

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Modular Design

Create reusable components for data loading, preprocessing, model training, and evaluation

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Testing Strategy

Unit tests for functions, integration tests for pipelines, and model validation tests

⚙️

Configuration Management

Externalize hyperparameters, paths, and settings using YAML or JSON files

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Documentation

Clear README, API docs, and inline comments explaining model choices and assumptions

Click on any practice to learn implementation details and see code examples!

Best Practices for Code Organization

Core Principles

ML Project Structure

Essential ML Code Organization Practices

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