Practical Considerations in Machine Learning

This section covers important practical aspects and considerations when developing and deploying machine learning systems.

Bias and Fairness

Understanding Bias

  • Types of Bias

    • Selection Bias
    • Sampling Bias
    • Confirmation Bias
    • Reporting Bias

  • Sources of Bias

    • Data Collection
    • Feature Selection
    • Model Design
    • Evaluation Metrics

Fairness Metrics

  • Group Fairness
  • Individual Fairness
  • Equality of Opportunity
  • Demographic Parity

Mitigation Strategies

  • Pre-processing Methods
  • In-processing Methods
  • Post-processing Methods
  • Fair Feature Selection

Data Leakage

Types of Leakage

  • Target Leakage
  • Train-Test Contamination
  • Temporal Leakage
  • Group Leakage

Prevention Strategies

  • Proper Data Splitting
  • Feature Engineering Guidelines
  • Cross-Validation Design
  • Pipeline Construction

Detection Methods

  • Statistical Tests
  • Feature Importance Analysis
  • Model Performance Analysis
  • Domain Knowledge Validation

Model Interpretability

Local Interpretability

  • LIME
  • SHAP Values
  • Counterfactual Explanations
  • Feature Attribution

Global Interpretability

  • Feature Importance
  • Partial Dependence Plots
  • Model Distillation
  • Rule Extraction

Trade-offs

  • Accuracy vs Interpretability
  • Complexity vs Simplicity
  • Speed vs Explainability
  • Generalization vs Specialization

Deployment Considerations

Infrastructure

  • Scalability
  • Resource Management
  • Version Control
  • Monitoring Systems

Model Serving

  • API Design
  • Batch Processing
  • Real-time Inference
  • Edge Deployment

Monitoring

  • Performance Metrics
  • Data Drift
  • Model Drift
  • System Health

Ethical Considerations

Privacy

  • Data Protection
  • Anonymization
  • Consent Management
  • Regulatory Compliance

Transparency

  • Model Documentation
  • Decision Explanation
  • Audit Trails
  • User Communication

Accountability

  • Error Handling
  • Impact Assessment
  • Incident Response
  • Stakeholder Engagement

Cost Considerations

Development Costs

  • Data Collection
  • Infrastructure Setup
  • Model Training
  • Team Resources

Operational Costs

  • Compute Resources
  • Storage
  • Maintenance
  • Updates and Improvements