Machine Learning with Apache Spark

  1. Advanced Topics and Deployment
    1. Model Persistence and Management
      1. Saving Models and Pipelines
        1. File System Storage
          1. Distributed Storage Systems
            1. Metadata Management
            2. Loading Models and Pipelines
              1. Version Compatibility
                1. Cross-Platform Portability
                  1. Dependency Management
                  2. Model Versioning
                    1. Version Control Strategies
                      1. Model Registry Integration
                        1. Rollback Capabilities
                        2. Cross-Language Portability
                          1. PMML Export
                            1. ONNX Integration
                              1. Custom Serialization
                            2. Real-time Machine Learning with Structured Streaming
                              1. Structured Streaming Overview
                                1. Stream Processing Concepts
                                  1. Micro-Batch Processing
                                    1. Continuous Processing
                                    2. Integrating ML Models with Streaming Data
                                      1. Model Application in Streams
                                        1. Stateful Stream Processing
                                          1. Windowed Operations
                                          2. Continuous Training and Inference
                                            1. Online Learning Approaches
                                              1. Model Updates
                                                1. Concept Drift Detection
                                                2. Stream Processing Patterns
                                                  1. Event Time Processing
                                                    1. Watermarking
                                                      1. Late Data Handling
                                                    2. Model Deployment and Serving
                                                      1. Deployment Strategies
                                                        1. Batch Inference
                                                          1. Scheduled Batch Jobs
                                                            1. Large-Scale Scoring
                                                              1. ETL Integration
                                                              2. Real-Time Inference
                                                                1. Low-Latency Serving
                                                                  1. API Endpoints
                                                                    1. Microservices Architecture
                                                                    2. Edge Deployment
                                                                      1. Model Compression
                                                                        1. Resource Constraints
                                                                      2. Model Serving Frameworks
                                                                        1. MLeap Integration
                                                                          1. Pipeline Export
                                                                            1. Serving Outside Spark
                                                                              1. Performance Benefits
                                                                              2. MLflow Integration
                                                                                1. Model Registry
                                                                                  1. Model Serving APIs
                                                                                    1. Experiment Tracking
                                                                                    2. Custom Serving Solutions
                                                                                    3. Production Considerations
                                                                                      1. Scalability Requirements
                                                                                        1. Fault Tolerance
                                                                                          1. Security Considerations
                                                                                            1. Monitoring and Alerting
                                                                                            2. A/B Testing and Experimentation
                                                                                              1. Model Comparison
                                                                                                1. Traffic Splitting
                                                                                                  1. Statistical Significance
                                                                                                    1. Performance Monitoring

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                                                                                                8. Model Evaluation and Hyperparameter Tuning

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                                                                                                10. Performance Tuning and Optimization