MLlib in Apache Spark

MLlib, maintained by the Apache Software Foundation as part of Apache Spark, is a scalable machine learning library enabling distributed ML across large datasets, updated and tested with every Spark release. It supports Java, Scala, Python, and R, with algorithms spanning classification (logistic regression, naive Bayes), regression, decision trees, random forests, gradient-boosted trees, clustering (K-means, Gaussian mixtures), recommendation (alternating least squares), and topic modeling (LDA) — plus ML pipeline construction, model evaluation, hyperparameter tuning, model persistence, and distributed linear algebra (SVD, PCA). Completely free and open source under the Apache License 2.0, with active recent releases (3.5.9, 4.0.4, 4.1.3, 4.2.0). Good fit for a data engineering team already running Apache Spark for big-data processing that wants machine learning capable of scaling across the same distributed infrastructure, rather than a separate ML system that can't handle Spark-scale data.