In-memory cluster computing for machine learning

The field of machine learning has evolved in recent years to incorporate large amounts of data over which intricate mathematical computations and algorithms are executed. Self-driving cars which facilitate basic transportation needs, robust search and recommendation engines simplifying online shopping, and fraud detection systems protecting individuals from digital theft represent a few highly successful applications of machine learning in which large data stores are needed. As applications of machine learning require larger volumes of data, the need for a faster data infrastructure increases in importance to reduce the latency and constraints that disk I/O places on models requiring numerous iterations through the data.