Machine Learning with Scikit-Learn

  1. Model Persistence and Deployment
    1. Model Serialization
      1. Importance of Model Persistence
        1. Serialization Formats
        2. Using joblib
          1. Saving Models
            1. Loading Models
              1. Compression Options
                1. Memory Mapping
                2. Using pickle
                  1. Python Object Serialization
                    1. Compatibility Considerations
                      1. Security Concerns
                      2. Version Compatibility
                        1. Scikit-learn Version Changes
                          1. Python Version Compatibility
                            1. Dependency Management
                            2. Model Deployment Strategies
                              1. Batch Prediction
                                1. Real-time Prediction
                                  1. API Integration
                                    1. Cloud Deployment
                                    2. Model Monitoring
                                      1. Performance Tracking
                                        1. Data Drift Detection
                                          1. Model Retraining
                                          2. Best Practices
                                            1. Model Versioning
                                              1. Documentation
                                                1. Testing Procedures
                                                  1. Rollback Strategies

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                                                12. Advanced Topics

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                                                14. Performance Optimization