Spark MLlib

Apache Spark is an open-source platform for large dataset processing. It is well suited for iterative machine learning tasks as it leverages in-memory data structures such as RDDs. MLlib is Spark's machine learning library. MLlib provides functionality for various learning algorithms-supervised and unsupervised. It includes various statistical and linear algebra optimizations. It is shipped along with Apache Spark and hence saves on installation headaches like some other libraries. MLlib supports several higher languages such as Scala, Java, Python and R. It also provides a high-level API to build machine-learning pipelines.

MLlib's integration with Spark has quite a few benefits. Spark is designed for iterative computation cycles; it enables efficient implementation platform for large machine learning algorithms, as these algorithms are themselves iterative.

Any improvement in Spark's data structures results in direct gains for MLlib. Spark's large community contributions have helped bring new algorithms to MLlib faster.

Spark also has other APIs such as Pipeline APIs GraphX, which can be used in conjunction with MLlib; it makes building interesting use cases on top of MLlib easier.