Apache Mahout is a project of the Apache Software Foundation which is implemented on top of Apache Hadoop and uses the MapReduce paradigm.
It is also used to create implementations of scalable and distributed machine learning algorithms that are focused in the areas of clustering, collaborative filtering and classification. Mahout contains Java libraries for common math algorithms and operations focused on statistics and linear algebra, as well as primitive Java collections.
Apache Mahout is all about machine learning and the project is aimed at making a powerful tool for building intelligent applications faster and easier.
This used to be the exclusive domain of academics and corporations with large research budgets, but in today’s data-driven world, the need for intelligent applications that can learn from data and user data is increasing.
Apache Mahout is used for creating applications with machine-learning techniques such as clustering, categorization, and collaborative filtering for finding commonalities in large data groups or for tagging large volumes of web content.
Scalable to large data sets - the core algorithms are implemented on large scalable, distributed systems.
Scalable to support different business cases - distributed under commercially friendly Apache Software License
Scalable community - there is a vast, vibrant, diverse and responsive community to facilitate discussions on the project and its potential use cases.