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Home > Blogs > Demystifying Mahout
The best way for tech professionals to stay at the top of their game is to never stop learning. Technology is constantly evolving and changing the way industries work. If you’ve completed online courses to get a Hadoop certification or Big Data Certification, then the next thing you should look into is Mahout.
What is Mahout?
The goal of the whole Apache Mahout™ project is to build an environment for quickly creating scalable machine learning on Hadoop.
If you’ve read about machine learning, then you would know that it aims to enable machines to learn without being explicitly programmed. It is done to improve or enhance future outcomes based on past performances.
It starts with big data being stored on the Hadoop Distributed File System (HDFS). Mahout then provides the data science tools required to find meaningful patterns in those big data sets. So it won’t be wrong to say that the Apache Mahout project is about turning big data into ready-to-use big information quickly and conveniently.
Picture Courtesy – Trifork
Features of Mahout
It has a simple and extendable programming environment that helps to build scalable algorithms.
- It offers plenty of premade algorithms for Scala + Apache Spark, H2O, Apache Flink.
- It has Samsara, which is a vector math experimentation environment with R-like syntax.
- How does Mahout help?
- If you are looking at Mahout in Data Science then its main uses include:
- Collaborative filtering – It collects all the data of the users to understand their behaviour and then makes product recommendations based on the collected information (e.g. product recommendations you get after visiting an online shopping store like Flipkart)
Picture courtesy - Remarkety
- Clustering – It takes items in a particular class like web pages or newspaper articles, etc. and then organizes them into naturally occurring groups. The aim is to make clusters where the items similar to each other are bundled in the same group.
- Classification – It learns from available existing categorizations and then assigns unclassified items to the most relevant category possible.•Frequent itemset mining – It analyses all the items in a particular group and then identifies which other items typically appear together with these items. For example, it will analyse the items you have placed in your cart and suggest what customers frequently buy with the item in question. For example, if you are buying a phone, you may see suggestions for a tempered glass or a phone cover, which most customers buy along with a phone.
How can it help professionals?
For one, Mahout can help those who are selling products and services to understand and predict their user’s behaviour based on their interactions on the site. Those who have completed professional courses in Mahout will be able to guess a user’s preference based on his/her interest in similar items.
Apart from this, it also helps to quickly estimate the similarity between two data sets. It is beneficial in discovering soft clusters where a particular point can belong to more than just one cluster.
Where is Mahout being used?
Companies such as Facebook, LinkedIn, Foursquare, Yahoo, and Twitter use Mahout. If you look at Facebook, you’ll see that suggested videos or posts that you might like are determined with the help of Mahout. Foursquare uses the Mahout recommendation engine to help you find places for food and entertainment in particular areas.
Picture Courtesy - medium
These are just a few examples of how and where Mahout is being used. Have you noticed Mahout being used on major websites? Drop us a comment if you’ve used Mahout or have seen it being used. We would also love to know how it has helped you. If you’re new to it, would you prefer a short-term course or a certification course on Mahout to learn more about it?
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