Difference between revisions of "Meteos"
(→Design & Use Cases) |
(→Projects) |
||
Line 7: | Line 7: | ||
== Projects == | == Projects == | ||
− | === Meteos === | + | ==== Meteos ==== |
{| border="1" cellpadding="2" | {| border="1" cellpadding="2" | ||
Line 20: | Line 20: | ||
|} | |} | ||
− | === Python Meteos Client === | + | ==== Python Meteos Client ==== |
{| border="1" cellpadding="2" | {| border="1" cellpadding="2" | ||
| Source code | | Source code |
Revision as of 02:49, 29 November 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.
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