Difference between revisions of "Meteos"
(→Examples) |
(→Design & Use Cases) |
||
Line 40: | Line 40: | ||
* [[Meteos/Usecases| Meteos Use Cases]] | * [[Meteos/Usecases| Meteos Use Cases]] | ||
− | * [[Meteos/Models| Meteos Prediction Models]] | + | * [[Meteos/Models| Meteos Datasets and Prediction Models]] |
== Getting Started with Meteos == | == Getting Started with Meteos == |
Revision as of 06:40, 4 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 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