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From Sally Khudairi ...@apache.org>
Subject The Apache Software Foundation Announces Apache® Mahout™ v0.13.0
Date Mon, 01 May 2017 10:00:29 GMT
[this announcement is available online at https://s.apache.org/ioAa ]

Open Source scalable machine learning and data mining library for Big
Data artificial intelligence now more powerful and easier to use.

Forest Hill, MD —1 May 2017— The Apache Software Foundation (ASF), the
all-volunteer developers, stewards, and incubators of more than 350 Open
Source projects and initiatives, announced today the availability of
Apache® MahoutTM v0.13.0, the latest version of the Open Source scalable
machine learning library.

Apache Mahout provides an environment for quickly creating
machine-learning applications that scale and run on the
highest-performance parallel computation engines available. Mahout is
the first scalable generalized tensor and linear algebra solving engine
taking data scientists from interactive experiments to production use.

"Apache Mahout 0.13.0 is more powerful with its new algorithm framework
that allows for easier implementation of machine learning algorithms,"
said Andrew Palumbo, Vice President of Apache Mahout. "The enhanced
Mahout code base and development framework make machine learning even
more accessible, which is a game changer in the field of artificial

Mahout provides a wide variety of premade algorithms (Matrix
Factorization, QR via ALS, SSVD, PCA, etc.) for Scala + Apache Spark,
H2O, and Apache Flink, as well as on-GPU compute for performance
improvements in very large tensor math. Apache Mahout provides the data
science tools to automatically find meaningful patterns in Big Data sets
by supporting the following main data science use cases:

Collaborative filtering – mines user behavior and makes product
recommendations (such as eCommerce product recommenders);
Regression – estimates a numerical value based on values of other
Clustering – takes items in a particular class (such as Web pages or
newspaper articles) and organizes them into naturally occurring groups,
such that items belonging to the same group are similar to each other;
Classifying – learns from existing categorizations and then assigns
unclassified items to the best category.

New in v0.13.0
Apache Mahout now makes it easier to do matrix math on graphics cards,
which is relevant for most modern machine-learning and deep-learning
methods. In addition, v0.13.0 allows shared nothing computation on GPUs,
on multi-core CPU, or in the JVM as appropriate, as well as a simplified
framework for building new algorithms. As Mahout comprises an
interactive environment and library that support generalized scalable
linear algebra and include many modern machine-learning algorithms, the
project has also collaborated with developers on other projects,
including the Open Source linear algebra library ViennaCL, the Java
wrapper library interface JavaCPP, and the graphics processor technology
manufacturer NVIDIA to add CUDA bindings directly into Mahout for
simplicity of development.

The v0.13.0 release reflects 62 separate JIRA issues from v0.12.2,
including numerous enhancements to Mahout-Samsara, the vector math
experimentation environment with R-like syntax that works at scale.
Complete release notes are at

Future versions of Mahout will include support for native iterative
solvers, a more robust algorithm library, and smarter probing and
optimization of multiplications, among other features.

A comprehensive list of users of Apache Mahout is available at
https://mahout.apache.org/general/powered-by-mahout.html ; current users
are mostly researchers and developers actively involved in building
distributed machine-learning pipelines and tools.

"We thank our community of developers and users who helped make this
milestone release possible, and welcome new contributors to help us
advance machine learning," added Palumbo.

Catch Apache Mahout in action at Apache: Big Data, where attendees learn
first-hand from many original project creators and companies from the
greater Mahout community. Apache: Big Data will be held 16-18 May 2017
in Miami, FL. To register, and for more information, visit

Availability and Oversight
Apache Mahout software is released under the Apache License v2.0 and is
overseen by a self-selected team of active contributors to the project.
A Project Management Committee (PMC) guides the Project's day-to-day
operations, including community development and product releases. For
downloads, documentation, and ways to become involved with Apache
Mahout, visit http://mahout.apache.org/ and

About The Apache Software Foundation (ASF)
Established in 1999, the all-volunteer Foundation oversees more than 350
leading Open Source projects, including Apache HTTP Server --the world's
most popular Web server software. Through the ASF's meritocratic process
known as "The Apache Way," more than 620 individual Members and 6,000
Committers successfully collaborate to develop freely available
enterprise-grade software, benefiting millions of users worldwide:
thousands of software solutions are distributed under the Apache
License; and the community actively participates in ASF mailing lists,
mentoring initiatives, and ApacheCon, the Foundation's official user
conference, trainings, and expo. The ASF is a US 501(c)(3) charitable
organization, funded by individual donations and corporate sponsors
including Alibaba Cloud Computing, ARM, Bloomberg, Budget Direct,
Capital One, Cash Store, Cerner, Cloudera, Comcast, Confluent, Facebook,
Google, Hortonworks, HP, Huawei, IBM, InMotion Hosting, iSigma,
LeaseWeb, Microsoft, ODPi, PhoenixNAP, Pivotal, Private Internet Access,
Produban, Red Hat, Serenata Flowers, Target, WANdisco, and Yahoo. For
more information, visit http://www.apache.org/ and

© The Apache Software Foundation. "Apache", "Flink", "Apache Flink",
"Mahout", "Apache Mahout", "Spark", "Apache Spark", and "ApacheCon" are
registered trademarks or trademarks of the Apache Software Foundation in
the United States and/or other countries. All other brands and
trademarks are the property of their respective owners.

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