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Difference between revisions of "Meteos"

(Meteos (Machine Learning as a Service))
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* [[Meteos/ExampleDecisionTree| Make a Decision by using DecisionTree Model]]  
 
* [[Meteos/ExampleDecisionTree| Make a Decision by using DecisionTree Model]]  
  
* [[Meteos/ExampleKmeans| Clustering User Preferences by using Kmeans Model]]  
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* [[Meteos/ExampleKmeans| Classify a User Preferences by using Kmeans Model]]  
  
 
* [[Meteos/ExampleRecommend| Recommend Movie by using Recommendation Model]]
 
* [[Meteos/ExampleRecommend| Recommend Movie by using Recommendation Model]]

Revision as of 03:21, 5 December 2016

Meteos (Machine Learning as a Service)

Meteos is Machine Learning as a Service (MLaaS) in Apache Spark.

Meteos allows users to analyze huge amount of data and predict a value by data mining and machine learning algorithms. Meteos create a workspace of Machine Learning via sahara spark plugin and manage some resources and jobs regarding Machine Learning.

Meteos is named from Meteo (Meteorologist) + OS (OpenStack).

Projects

Meteos

Source code https://github.com/openstack/meteos
Bug tracker https://bugs.launchpad.net/meteos
Feature tracker https://blueprints.launchpad.net/meteos

Python Meteos Client

Source code https://github.com/openstack/python-meteosclient
Bug tracker https://bugs.launchpad.net/python-meteosclient
Feature tracker https://blueprints.launchpad.net/python-meteosclient

Design & Use Cases

Getting Started with Meteos

Instructions for getting started with Meteos using Devstack are available at: Meteos on Devstack

API

Examples