An Introduction To Apache Mahout Project

Apache Mahout is an algorithm library by Apache Software Foundation for  scalable machine learning applications. It is a library of scalable machine learning algorithms which is often implemented on the top of Apache Hadoop using the MapReduce pattern. Machine learning is a technique in artificial intelligence that enable machines to learn without the use of a programming languages. The primary objective of Artificial Intelligence(AI) is to improve future performance of the applications based on the previous results.

How Mahout Functions?

Mahout actually allows data science tools to automatically find meaningful patterns in big data sets that are stored on the Hadoop Distributed File System(HDFS).  Mahout project is also useful in a way that it makes it faster and convenient to transform big data into useful information.

Some Distinctive Features Of Apache Mahout

• Allows Collaborative filtering i.e. it mines user behavior and makes probable product recommendations.

• Allows Clustering which organizes items in a particular class and and naturally occurring groups in such a way that all the items belonging to the same group share similar characteristics

• Allows automatic Classification by learning from existing categories and  assigns unclassified items to the best category accordingly

• Another delighting feature includes Frequent itemset mining that analyzes items in a group i.e. similar to items in a shopping cart and then identifies which items typically appear together

• Incorporates a number of pre designed algorithms for Scala + Apache Spark, H2O, Apache Flink

• Contains Samsara which is a vector math experimentation environment with R-like syntax which works at scale

So far, there has been a number of releases and upgradations in the Mahout Project. Since its official launch in early 2012, about more than 10 upgraded versions of Mahout have already been released.