Tabla de Contenidos: “…Fixed Window Aggregation over an Event Time -- Sliding Window Aggregation over Event Time -- Aggregation State -- Watermarking: Limit State and Handle Late Data -- Arbitrary Stateful Processing -- Arbitrary Stateful Processing with Structured Streaming -- Handling State Timeouts -- Arbitrary State Processing in Action -- Extracting Patterns with mapGroupsWithState -- User Sessionization with flatMapGroupsWithState -- Handling Duplicate Data -- Fault Tolerance -- Streaming Application Code Change -- Spark Runtime Change -- Streaming Query Metrics and Monitoring -- Streaming Query Metrics -- Monitoring Streaming Queries via Callback -- Monitoring Streaming Queries via Visualization UI -- Streaming Query Summary Information -- Streaming Query Detailed Statistics Information -- Troubleshooting Streaming Query -- Summary -- Chapter 8: Machine Learning with Spark -- Machine Learning Overview -- Machine Learning Terminologies -- Machine Learning Types -- Supervised Learning -- Unsupervised Learning -- Reinforcement Learning -- Machine Learning Development Process -- Spark Machine Learning Library -- Machine Learning Pipelines -- Transformers -- Estimators -- Pipeline -- Pipeline Persistence: Saving and Loading -- Model Tuning -- Speeding Up Model Tuning -- Model Evaluators -- Machine Learning Tasks in Action -- Classification -- Model Hyperparameters -- Example -- Regression -- Model Hyperparameters -- Example -- Recommendation -- Model Hyperparameters -- Example -- Deep Learning Pipeline -- Summary -- Chapter 9: Managing the Machine Learning Life Cycle -- The Rise of MLOps -- MLOps Overview -- MLflow Overview -- MLflow Components -- MLflow in Action -- MLflow Tracking -- MLflow Projects -- MLflow Models -- MLflow Model Registry -- Model Deployment and Prediction -- Summary -- Index…”

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