Businesses can utilize Apache Mahout to develop both supervised and unsupervised machine learning systems that are scalable. Supervised machine learning functions collect specific training data and store classified information. Unsupervised learning takes in data in less defined formats. Either way, the system is developing active results based on input.
One use of Apache Mahout is for the practice of collaborative filtering, which is a popular means by which retailers build recommendation engines or other deep learning systems that try to figure out personalized customer preference. Different kinds of collaborative filtering setups such as user-based or item-based systems are attractive for businesses that want to boost conversion and outreach to customers – Apache Mahout can be used for any of these types of projects. For example, businesses can feed user and product data into a machine learning system to get better business intelligence and chart a path forward, based on customer histories and profiles as well as other useful data.
Companies may also use Apache Mahout for data clustering. Essentially, the Apache Mahout tool breaks down the large data sets and sorts them into likely groups, and uses various metrics and algorithms to figure out which values and variables belong together.
A similar approach, categorization, is also something that Apache Mahout can help with. Apache Mahout can implement clustering tools based on Apache MapReduce, or work with matrix and vector libraries, or use Bayesian classification systems.
Typically, companies create teams to write and input code, to create recommendation engines or other tools based on machine learning processes. Apache Mahout can help with a lot of the legwork of organizing and implementing these projects.
Through the use of helpful templates and libraries, Apache Mahout can help with the compilation of resources and experimental models for creating recommendation engines and other useful business-related items. Professionals may also use Apache Mahout in trying to figure out how to manage growth or scale systems on an ongoing basis, according to enterprise needs.