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•Distributed Algebraic optimizer •R-Like DSL Scala API •Linear algebra operations •Ops are extensions to Scala •Scala REPL based interactive shell •Integrates with compatible libraries like MLLib •Runs on distributed Spark, H2O, and Flink •Fastutil to speed up sparse matrix and vector computations
What are the benefits?
• Access to extensible programming framework • Build scalable algorithms • Access many premade algorithms • Access to a math experimentation environment • Provides fault tolerance in case of failure
PAT Rating™
Editor Rating
Aggregated User Rating
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Ease of use
7.4
7.0
Features & Functionality
7.6
8.7
Advanced Features
7.6
8.9
Integration
7.5
8.8
Performance
7.4
8.1
Customer Support
7.5
8.7
Implementation
8.9
Renew & Recommend
10
Bottom Line
Apache Mahout introduces a new math environment called Samsara, for its theme of universal renewal. It reflects a fundamental rethinking of how scalable machine learning algorithms are built and customized.
7.5
Editor Rating
8.7
Aggregated User Rating
10 ratings
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The Apache Mahout project’s goal is to build an environment for quickly creating scalable performant machine learning applications. Apache Mahout is a simple and extensible programming environment and framework for building scalable algorithms and contains a wide variety of premade algorithms for Scala and Apache Spark, H2O, Apache Flink.
It also used Samsara which is a vector math experimentation environment with R-like syntax which works at scale. Apache™ Mahout is a library of scalable machine-learning algorithms, implemented on top of Apache Hadoop and using the MapReduce paradigm. While Mahout's core algorithms for clustering, classification and batch based collaborative filtering are implemented on top of Apache Hadoop using the map/reduce paradigm, it does not restrict contributions to Hadoop-based implementations. Contributions that run on a single node or on a non-Hadoop cluster are also welcomed.
For example, the 'Taste' collaborative-filtering recommender component of Mahout was originally a separate project and can run stand-alone without Hadoop. 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. Apache Mahout is a great programming aid that will be useful whether the user is a beginner or more advanced. Easy tutorials are also available online.
Simple and extensible programming environment and framework for building scalable algorithms
Company size
Small (<50)
User Role
End User
User Industry
Pharmaceutical
Rating
Ease of use8.2
Features & Functionality8.1
Advanced Features8.3
Integration8.2
ADDITIONAL INFORMATION Apache mahout provides a simple and extensible programming environment and framework for building scalable algorithms. Apache Mahout also provides robust matrix decomposition algorithms as well as a Naive Bayes classifier and collaborative filtering. This enables the next generation of co-occurrence recommenders to use entire user click streams and context in making recommendations. It also features a scikit-learn-like framework for algorithms with the goal for creating a consistent API for various machine-learning algorithms and an orderly package structure for grouping regression, classification, clustering, and pre-processing algorithms together. Apache Mahout’s math environment called Samsara, is a fundamental rethinking of how scalable machine learning algorithms are built and customized. Mahout-Samsara helps developers create their own math while providing new algorithm implementations built for speed on Mahout-Samsara. They run on Spark 1.3+, Flink 1.0.1, and some on H2O, which increases speed by about time times. The core of Mahout-Samsara features general linear algebra and statistical operations along with the data structures to support them. You can use is as a library or customize it in Scala with Mahout-specific extensions that look like R-code. Mahout-Samsara comes with an interactive shell that runs distributed operations on an Apache Spark cluster. This makes prototyping or task submission much easier and allows users to customize algorithms with a whole new degree of freedom.
Simple and extensible programming environment and framework for building scalable algorithms
Small (<50)
End User
Pharmaceutical
ADDITIONAL INFORMATION
Apache mahout provides a simple and extensible programming environment and framework for building scalable algorithms. Apache Mahout also provides robust matrix decomposition algorithms as well as a Naive Bayes classifier and collaborative filtering. This enables the next generation of co-occurrence recommenders to use entire user click streams and context in making recommendations. It also features a scikit-learn-like framework for algorithms with the goal for creating a consistent API for various machine-learning algorithms and an orderly package structure for grouping regression, classification, clustering, and pre-processing algorithms together. Apache Mahout’s math environment called Samsara, is a fundamental rethinking of how scalable machine learning algorithms are built and customized. Mahout-Samsara helps developers create their own math while providing new algorithm implementations built for speed on Mahout-Samsara. They run on Spark 1.3+, Flink 1.0.1, and some on H2O, which increases speed by about time times. The core of Mahout-Samsara features general linear algebra and statistical operations along with the data structures to support them. You can use is as a library or customize it in Scala with Mahout-specific extensions that look like R-code. Mahout-Samsara comes with an interactive shell that runs distributed operations on an Apache Spark cluster. This makes prototyping or task submission much easier and allows users to customize algorithms with a whole new degree of freedom.