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Maintenable Code

Updated May 13, 2023 ·

Overview​

Good code structure, versioning, and documentation make ML projects easier to manage and adapt.

Organizing ML Projects​

A clear project structure keeps files easy to find.

  • Group related files (data, models, scripts)
  • Use clear names for easy identification
  • Separate concerns (preprocessing, training, evaluation)

This makes collaboration and debugging much simpler.

Sample Project Structure​

A well-organized ML project follows a logical structure:

ml_project/
│── data/
│   ├── raw/
│   ├── processed/
│   ├── interim/
│── models/
│── notebooks/
│── src/
│   ├── preprocessing.py
│   ├── feature_engineering.py
│   ├── training.py
│── README.md

Where:

  • data/ stores raw and processed datasets
  • models/ contains trained models
  • notebooks/ helps explore and visualize data
  • src/ includes core scripts for ML tasks
  • README.md explains how to use the project

Tracking Code Changes​

Version control helps keep track of updates and fixes.

  • Revert changes if something breaks
  • Find errors faster by comparing old versions
  • Work in parallel with teammates easily

Using Git for version control makes ML development more efficient.

Clear Documentation​

Good documentation helps others (and your future self) understand the project.

  • Explain files and functions (what they do and how to use them)
  • Provide setup and deployment instructions
  • Keep comments in code to describe important logic

Here is an example of a well-documented function :

def clean_data(df):
    """
    Cleans input DataFrame by removing null values and duplicates.
    
    Args:
        df (pd.DataFrame): The raw dataset.
    
    Returns:
        pd.DataFrame: The cleaned dataset.
    """
    df = df.dropna().drop_duplicates()
    return df

This makes it easier for anyone to understand and modify the function.

Keeping Code Adaptable​

Maintainable code is easy to update and expand.

  • Well-structured code is easier to modify
  • Clear documentation prevents confusion
  • Scalability helps handle changing data and requirements

Clean, well-organized ML code allows projects to evolve smoothly over time.