apache mahout vs spark

https://www.wikitechy.com/interview-questions/tag/apache-mahout-vs-spark/ Interview Questions Thu, 09 Sep 2021 05:58:36 +0000 en-US hourly 1 https://wordpress.org/?v=6.9 https://www.wikitechy.com/interview-questions/wp-content/uploads/2025/10/cropped-wikitechy-icon-32x32.png apache mahout vs spark - Wikitechy https://www.wikitechy.com/interview-questions/tag/apache-mahout-vs-spark/ 32 32 What is the difference between Apache Mahout and Spark MLLib ? https://www.wikitechy.com/interview-questions/mahout/what-is-the-difference-between-apache-mahout-and-spark-mllib/ https://www.wikitechy.com/interview-questions/mahout/what-is-the-difference-between-apache-mahout-and-spark-mllib/#respond Tue, 20 Jul 2021 03:18:20 +0000 https://www.wikitechy.com/interview-questions/?p=1078

Differences between Apache Mahout and Spark MLLib:

Apache Mahout is a multi-backend capable high level system with implementations of some scalable algorithms.
These fundamentally include large-scale matrix decomposition and recommendation algorithms, yet any linear algebra based issue can be attacked with Mahout.
Efficient implementation is likely at this point to require improvement of the current optimizer (which is relatively easy to do), but when the optimizer is able to simplify the program, the results are quite dramatic.
Mahout also supports both Spark and H2O back-ends while MLLib is bound only to Spark.
If Spark manages to dominate all other possible back-ends (such as H2O, Julia or many others) then it is likely that MLLib will do as well as Mahout on performance.
On the other hand, with back ends that have super high performance specialized capabilities, Mahout will give you port programs to utilize these capabilities very efficiently.
MLLib is new and has yet to really hit its stride while Mahout is older and has belongings from the earlier period.
These to characteristics are opposite sides of the similar coin. Mahout focuses on algorithms for which there is a wide-spread need for scalable algorithms. MLLib is considerably more liberal with what is gotten.

Mahout and MLLib both have a typical execution engine.
Apache Mahout focuses on machine learning and have a rich set of algorithms, while MLLib only adopt various develop and basic algorithms.

Differences between apach mahout and spark mllib

]]> https://www.wikitechy.com/interview-questions/mahout/what-is-the-difference-between-apache-mahout-and-spark-mllib/feed/ 0 What is pre-requisites for contributing to Apache Mahout ? https://www.wikitechy.com/interview-questions/mahout/what-is-pre-requisites-for-contributing-to-apache-mahout/ https://www.wikitechy.com/interview-questions/mahout/what-is-pre-requisites-for-contributing-to-apache-mahout/#respond Tue, 20 Jul 2021 03:16:51 +0000 https://www.wikitechy.com/interview-questions/?p=1077

Pre-requisites for contributing to Apache Mahout:

Apache Mahout is open project. There are lots of new things happening in the project lately so a variety of skills are useful.
If you have a bit of background in machine learning or math or Spark or Scala, then your contributions would be quite welcoming.
Even if you just would like to work through and correct some of the tutorials that might have gotten a bit out of date, your contributions would be very welcoming.

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Uses of mahout:

It uses the Apache Hadoop library to allow Mahout to scale efficiently in the cloud.
Mahout focus on real-world, practical use cases as opposed to bleeding-edge research or unproven techniques and offers quality documentation.
It used to produce scalable machine learning algorithm then to implements its machine learning techniques that are:
1. Recommendation
2. Clustering
3. Classification

Applications of Mahout:

Adobe, Facebook, LinkedIn, Foursquare, Twitter, and Yahoo use Mahout internally.
Foursquare helps you to find out the place, food and entertainment available in a particular area. It uses the recommender engine of Mahout.
Twitter may use Mahout for user interest modelling.
Yahoo uses Mahout for pattern mining.

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Apache mahout:

An Apache Software Foundation project to create free implementations of distributed or else scalable machine learning algorithms under the Apache Software license that focused in the areas of collaborative filtering, classification and clustering.
Several implementations utilize the Apache Hadoop platform.

It gives Java libraries for general maths operations (like linear algebra framework, statistics and data scientists) and primal Java collections.
Mahout is a work in advancement; much number of implemented algorithms has developed rapidly, however various algorithms are still missing.
Apache Mahout contains three major features:
- It gives simple and extensible programming environment then framework for building versatile algorithms.
- A wide variety of premade algorithms for Scala + Apache Spark, Apache Flink, H2O.
- Samsara, a vector math experimentation environment with R-like syntax which works at scale.

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