Machine Learning Best Practices

You can find a more in-depth guide on best practices in machine learning here.

Data Management

Data Collection and Preparation

  • Ensure data quality and consistency
  • Document data sources and collection methods
  • Implement proper data versioning
  • Handle missing values appropriately
  • Normalize or standardize features when necessary
Tip: Always keep a copy of the raw data before any preprocessing steps.

Model Development

Model Selection and Training

  • Start with simple models before moving to complex ones
  • Use cross-validation for model evaluation
  • Implement proper train-test-validation splits
  • Monitor for overfitting and underfitting
  • Document model architecture and hyperparameters
Warning: Avoid overfitting by using regularization techniques and monitoring validation metrics.

Code Organization

Project Structure and Version Control

  • Use a consistent project structure
  • Implement proper version control (Git)
  • Write clean, documented code
  • Use virtual environments for dependencies
  • Create reproducible experiments

Model Deployment

Production Considerations

  • Implement proper model versioning
  • Monitor model performance in production
  • Set up automated retraining pipelines
  • Implement proper error handling
  • Consider model interpretability
Tip: Always have a rollback strategy for model deployments.