Mahout - Wikitechy

What is the difference between apache mahout and Prediction.io ?

Editor — Tue, 20 Jul 2021 03:21:23 +0000

Difference between Prediction.io and apache mahout

Apache mahout	Prediction.io
FEATURES: The three components of Mahout are an environment for building scalable algorithms,many new Scala + Spark and H2O (Apache Flink in progress) algorithms, and Mahout’s mature Hadoop MapReduce algorithms.	FEATURES: The Prediction.io supports event collection, evaluation, deployment of algorithms, querying predictive results via REST APIs.
PROGRAMMING LANGUAGES: Java, Python, Ruby	PROGRAMMING LANGUAGES: Java
SOURCE TYPE: Open	SOURCE TYPE: Open
Individual can create analytics apps with Mahout, if you want the apps as an API or as a server, individual needs something like Prediction.IO	Prediction.io wants an algorithm layer; it comes with Apache Spark as well as Stanford NLP, but individual also can use Mahout.

What is the difference between Cloudera Oryx and Apache Mahout ?

Editor — Tue, 20 Jul 2021 03:20:00 +0000

Differences between Cloudera Oryx and Apache Mahout

There are 3 broad things an operational ML system needs to do eventually
- Build models at scale, offline
- Update models in near real time
- Query models in real time
Most of the tools like Mahout or MLLib do building models at scale only.

Oryx tries to do all 3, and is not doing building model.
Therefore it is really intended as a complement to any Hadoop-based model build system.
As a result it is MapReduce based for model building and implemented algorithms instead of using Mahout to improve on perceived problems.
The project which is open source, is more designed as 3 complete apps rather than a platform for extension.
It only implements
- ALS for recommendation
- Kmeans for clustering
- Random decision forests for classification and regression
The major difference is fewer algorithms but complete apps including incremental update and serving. It is not the algorithms that are really the difference since Oryx is not a new library.
The next version is built on Spark and Kafka then becomes more of generic lambda architecture for ML that happens to have entire apps too.
It is kind of Summing bird for ML on Spark. It has no algorithms implementations at all, not now. Therefore it is even more different from Mahout or MLLib.

What is the difference between Apache Mahout and Spark MLLib ?

Editor — Tue, 20 Jul 2021 03:18:20 +0000

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.

What is pre-requisites for contributing to Apache Mahout ?

Editor — Tue, 20 Jul 2021 03:16:51 +0000

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.

How is 0xdata’s H2O different from Apache Mahout ?

Editor — Tue, 20 Jul 2021 03:15:13 +0000

0xdata’s H2O different from Apache Mahout:

By using H2O, you can use several of R, REST/JSON. For Example curl/bash, GUI browser, Scala or Java.
The H2O algorithms are usually 100x to 1000x faster than current Map/Reduce-based Mahout (but Mahout is working hard to port itself to both Spark and H2O).
H2O has some algorithms implemented:
1. Deep Learning / Neural Nets
2. Random Forest
3. Gradient Boosted Method
4. Generalized Linear Modelling including Logistic, Regression, Poisson, Gamma
5. PCA
6. KMeans (KMeans++, KMeans||, K-modes)
7. Naive Bayes
8. General Data Munging any standard R expression which auto-broadens to cover arrays also works the same in H2O.

What is the difference between GraphLab and Mahout ?

Editor — Tue, 20 Jul 2021 03:13:48 +0000

Difference between graphlab and mahout:

Mahout	Graphlab
Mahout is a framework for machine learning and part of the Apache Foundation	Graphlab project takes a quite different approach to parallel collaborative filtering (more broadly, machine learning), and is primarily used by academic institutions.
Mahout has inherent Fault-tolerance	Graphlab does not have inherent Fault-tolerance
Mahout looks like a more polished product, especially as it relies on Hadoop for scalability and distribution.	Graphlab excells since it is built ground up for iterative algorithms such as those used in collaborative filtering.
The mahout framework comes in two approaches: Online where recommendations are computed on demand, typically on smaller datasets. Offline which utilise Apache Hadoop to achieve scalability.	Graphlab lacks a production-ready distribution framework.
For 50000 items, you need to have N machines with at least 28 GiB of memory for each, where N is the number of Hadoop nodes and hence 28 GiB of memory becomes an issue.	Costly performance penalties since runtime of each phase is decided by slowest machine.

How Mahout used with Python ?

Editor — Tue, 20 Jul 2021 02:48:20 +0000

Mahout is used with Python:

You should need to download and install the JPype package for python.The initial step is to set up JPype is determining the path to the dynamic library for the jvm ; on linux this will be a .so file and on windows it will be a .dll.
In python script, make a global variable with the path to this dll file.
Then we need to make sense how we have to set the classpath for mahout. The simplest way to do this is to edit script in “bin/mahout” to print out the classpath. Now include the code line “echo $CLASSPATH” to the script anywhere in the following comment “run it”.
Finally execute the script to print out the classpath. Now copy this output and paste into a variable in your python script.
Presently we can create a function to begin the jvm in python utilizing jype.

from jpype import *
jvm=None
def start_jpype():
global jvm
if (jvm is None):
cpopt="-Djava.class.path={cp}".format(cp=classpath)
startJVM(jvmlib,"-ea",cpopt)
jvm="started"

In the same way while reading or writing call the JPype function:

start_jpype()

What are the uses and applications of Mahout ?

Editor — Tue, 20 Jul 2021 02:45:34 +0000

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.

What is Apache Mahout ?

Editor — Tue, 20 Jul 2021 02:43:50 +0000

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.