Mllib Distributed Machine Learning With Ml Pipeline Api

Unified engine for large-scale data processing and ML.

Unique: Implements ML Pipeline abstraction (Transformer/Estimator pattern) that serializes entire workflows to Parquet, enabling reproducible training and deployment; uses RDD/DataFrame operations for distributed training without requiring explicit distributed algorithms

vs others: More scalable than scikit-learn for large datasets because training is distributed; more reproducible than custom distributed training code because pipelines serialize completely including hyperparameters