MLlib: Machine Learning in Apache Spark

TLDR

Apache Spark is a popular open‑source platform for large‑scale data processing that is well‑suited for iterative machine learning tasks. In this paper we present MLlib, Spark's open‑source distributed machine learning library. MLlib offers efficient, language‑agnostic APIs built on Spark, incorporating statistical, optimization, and linear‑algebra primitives to simplify end‑to‑end machine learning pipelines. MLlib’s rapid growth, driven by a community of over 140 contributors, is supported by extensive documentation that enables quick user onboarding.

Abstract

Apache Spark is a popular open-source platform for large-scale data processing that is well-suited for iterative machine learning tasks. In this paper we present MLlib, Spark's open-source distributed machine learning library. MLlib provides efficient functionality for a wide range of learning settings and includes several underlying statistical, optimization, and linear algebra primitives. Shipped with Spark, MLlib supports several languages and provides a high-level API that leverages Spark's rich ecosystem to simplify the development of end-to-end machine learning pipelines. MLlib has experienced a rapid growth due to its vibrant open-source community of over 140 contributors, and includes extensive documentation to support further growth and to let users quickly get up to speed.