Great Introductions by author for spark. And I just screenshoted the important and clear three concepts diagram for references :)

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Step Snap 1: [Understanding Spark's ML Model Application]

In this data flow diagram, "Spark Apply ML" represents two main functionalities:

1. Model Serving

# Loading and applying model in Spark
from pyspark.ml import PipelineModel

# Load trained model
loaded_model = PipelineModel.load("path_to_model")

# Create Spark streaming for model application
def apply_model(streaming_df):
    # Make predictions on real-time data
    predictions = loaded_model.transform(streaming_df)
    return predictions

2. Batch Prediction

# Spark batch prediction example
from pyspark.sql import SparkSession

spark = SparkSession.builder.appName("ModelApplication").getOrCreate()

def batch_predict():
    # Read data for prediction
    data = spark.read.parquet("input_data")

    # Apply model for predictions
    predictions = loaded_model.transform(data)

    # Save prediction results
    predictions.write.parquet("predictions_output")

Key Advantages

  1. Distributed Processing
// Spark can process large-scale prediction tasks in distributed manner
predictions = spark.sparkContext.parallelize(data)\\\\
    .mapPartitions(lambda partition: model.predict(partition))
  1. Resource Optimization
# Configure Spark resources to optimize model application
spark.conf.set("spark.executor.memory", "10g")
spark.conf.set("spark.executor.cores", "4")
  1. Integrated Data Processing
# End-to-end data processing and model application
def end_to_end_pipeline():
    # Data preprocessing
    processed_data = spark.sql("""
        SELECT * FROM raw_data
        WHERE quality > 0.8
    """)

    # Apply model
    results = loaded_model.transform(processed_data)

    # Post-processing
    final_results = results.select(
        "prediction",
        "probability",
        "features"
    )

Use Cases

  1. Real-time Prediction Service
  2. Batch Prediction Processing