Data Processing

➺ Pipeline Components:

  • Data cleaning
  • Feature engineering
  • Normalization
  • Augmentation

    • ➺ Technical Details:

    Preprocessing Steps
  • Missing value handling
  • Outlier detection
  • Feature scaling
  • Encoding strategies
    • Feature Engineering
  • Automated feature extraction
  • Domain-specific features
  • Feature crossing
  • Dimensionality reduction
    • Data Quality
  • Bias detection
  • Class imbalance handling
  • Data validation
  • Version control

    • ➺ Production Considerations:

  • Pipeline scalability
  • Real-time processing
  • Feature stores
  • Data drift monitoring

    • ➺ Best Practices:

    Performance Optimization
  • Caching strategies
  • Parallel processing
  • Memory management
  • I/O optimization
    • Quality Assurance
  • Data validation
  • Schema enforcement
  • Unit testing
  • Monitoring systems

    • ➺Each section includes:

    1. Core concepts and fundamentals
    2. Technical implementations
    3. Practical considerations
    4. Best practices and monitoring
    5. Real-world applications