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Apache™ Mahout is a library of scalable machine-learning algorithms, implemented on top of Apache Hadoop® and using the MapReduce paradigm. Machine learning is a discipline of artificial intelligence focused on enabling machines to learn without being explicitly programmed, and it is commonly used to improve future performance based on previous outcomes.
Once big data is stored on the Hadoop Distributed File System (HDFS), Mahout provides the data science tools to automatically find meaningful patterns in those big data sets. The Apache Mahout project aims to make it faster and easier to turn big data into big information.
Mahout supports four main data science use cases:
The Mahout project was started by several people involved in the Apache Lucene (open source search) community with an active interest in machine learning and a desire for robust, well-documented, scalable implementations of common machine-learning algorithms for clustering and categorization. The community was initially driven by Ng et al.’s paper “Map-Reduce for Machine Learning on Multicore” (see Resources) but has since evolved to cover much broader machine-learning approaches. Mahout also aims to:
Although relatively young in open source terms, Mahout already has a large amount of functionality, especially in relation to clustering and CF. Mahout’s primary features are:
Unless you are highly proficient in Java, the coding itself is a big overhead. There’s no way around it, if you don’t know it already you are going to need to learn Java and it’s not a language that flows! For R users who are used to seeing their thoughts realized immediately the endless declaration and initialization of objects is going to seem like a drag. For that reason I would recommend sticking with R for any kind of data exploration or prototyping and switching to Mahout as you get closer to production.
Below is a current list of machine learning algorithms exposed by Mahout.
The next major version, Mahout 1.0, will contain major changes to the underlying architecture of Mahout, including:
The main difference will came from underlying frameworks. In case of Mahout it is Hadoop MapReduce and in case of MLib it is Spark. To be more specific – from the difference in per job overhead
If Your ML algorithm mapped to the single MR job – main difference will be only startup overhead, which is dozens of seconds for Hadoop MR, and let say 1 second for Spark. So in case of model training it is not that important.
Things will be different if your algorithm is mapped to many jobs. In this case we will have the same difference on overhead per iteration and it can be game changer.
Let’s assume that we need 100 iterations, each needed 5 seconds of cluster CPU.
In the same time Hadoop MR is much more mature framework then Spark and if you have a lot of data, and stability is paramount – I would consider Mahout as serious alternative.
Mahout supports several clustering-algorithm implementations, all written in Map-Reduce, each with its own set of goals and criteria:
Mahout Related Tutorials
|Adv Java Tutorial||Hadoop Tutorial|
|Apache Pig Tutorial||Apache Kafka Tutorial|
|Apache Ant Tutorial||Apache ZooKeeper Tutorial|
|MongoDB Tutorial||Apache Struts 2 Tutorial|
Mahout Related Interview Questions
|Adv Java Interview Questions||Hadoop Interview Questions|
|Apache Pig Interview Questions||Apache Kafka Interview Questions|
|Apache Ant Interview Questions||Apache ZooKeeper Interview Questions|
|MongoDB Interview Questions||Advanced SAS Interview Questions|
|Apache Struts 2 Interview Questions||Base Sas Interview Questions|
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