Difference between revisions of "Meteos"
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* [[Meteos/ExampleDecisionTree| Make a Decision by using DecisionTree Model]] | * [[Meteos/ExampleDecisionTree| Make a Decision by using DecisionTree Model]] | ||
− | * [[Meteos/ExampleKmeans| | + | * [[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
Contents
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