So far, we have discussed key machine learning techniques, such as clustering, classification, and recommendations. However, there are several machine learning libraries, such as MATLAB, R, and Weka out there to implement the preceding techniques.
The volume of available information is growing at an alarming rate. Most of the time, analyzing enormous datasets causes processors to run out of memory. Hence, processing large datasets or datasets with an exponential growth potential is a key challenge in modern machine learning applications.
The key characteristic that makes Apache Mahout shine out from other machine learning libraries is its ability to scale.
In this chapter, you will see how Apache Mahout achieves scalability in a production environment with Apache Hadoop.
