The paper introduces MLlib, the distributed machine learning library of Apache Spark, which is designed for large-scale data processing and iterative machine learning tasks. MLlib provides efficient functionalities for various learning settings, including classification, regression, collaborative filtering, clustering, and dimensionality reduction. It includes statistical, optimization, and linear algebra primitives and supports multiple programming languages (Java, Scala, Python, and R). MLlib's integration with Spark leverages Spark's ecosystem to simplify the development of end-to-end machine learning pipelines. The library has seen rapid growth due to its active open-source community, with over 140 contributors. Key features of MLlib include fast and scalable implementations of standard learning algorithms, algorithmic optimizations, a pipeline API for multi-stage learning pipelines, and tight integration with Spark's core components. Performance and scalability tests demonstrate that MLlib outperforms traditional MapReduce-based systems like Apache Mahout on moderately sized datasets. The paper also highlights the community support and documentation available for MLlib, emphasizing its role in the broader Spark ecosystem.The paper introduces MLlib, the distributed machine learning library of Apache Spark, which is designed for large-scale data processing and iterative machine learning tasks. MLlib provides efficient functionalities for various learning settings, including classification, regression, collaborative filtering, clustering, and dimensionality reduction. It includes statistical, optimization, and linear algebra primitives and supports multiple programming languages (Java, Scala, Python, and R). MLlib's integration with Spark leverages Spark's ecosystem to simplify the development of end-to-end machine learning pipelines. The library has seen rapid growth due to its active open-source community, with over 140 contributors. Key features of MLlib include fast and scalable implementations of standard learning algorithms, algorithmic optimizations, a pipeline API for multi-stage learning pipelines, and tight integration with Spark's core components. Performance and scalability tests demonstrate that MLlib outperforms traditional MapReduce-based systems like Apache Mahout on moderately sized datasets. The paper also highlights the community support and documentation available for MLlib, emphasizing its role in the broader Spark ecosystem.