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Difference between revisions of "Meteos"

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== Hibana (Machine Learning as a Service) ==
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== Meteos (Machine Learning as a Service) (Deprecated) ==
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Hibana is Machine Learning as a Service (MLaaS) in Apache Spark.
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(check the new design and mission of [[Meteos/Meteos2.0]])
  
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=== Projects ===
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Meteos is Machine Learning as a Service (MLaaS) in Apache Spark.
  
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TBD
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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.
  
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=== Use Cases ===
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Meteos is named from Meteo (Meteorologist) + OS (OpenStack).
  
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Machine Learning consists of the following phases.
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[[File:Meteos-overview.png]]
  
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* Learning Phase - Analyze huge amounts of data and create a Prediction Model
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== Projects ==
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* Prediction Phase - Predict a value according to the input value by using Prediction Model
 
  
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==== Use Cases in Learning Phase ====
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==== Meteos ====
  
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* Upload Raw Data - Upload a raw data to Object Stroage
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{| border="1" cellpadding="2"
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* Parse Raw Data - Parse a raw data to enable MLlib (Apache Spark's scalable machine learning library) to handle it. Users are allowed to parse a parsed data again.
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| Source code
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* Create Prediction Model - Create a Prediction Model by using MLlib. This Model supports model export to Predictive Model Markup Language (PMML).
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| https://github.com/openstack/meteos
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|-
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| Bug tracker
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| https://bugs.launchpad.net/meteos
 +
|-
 +
| Feature tracker
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| https://blueprints.launchpad.net/meteos
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|}
  
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==== Use Cases in Prediction Phase ====
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==== Python Meteos Client ====
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{| border="1" cellpadding="2"
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| Source code
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| https://github.com/openstack/python-meteosclient
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|-
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| Bug tracker
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| https://bugs.launchpad.net/python-meteosclient
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|-
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| Feature tracker
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| https://blueprints.launchpad.net/python-meteosclient
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|}
  
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* Predict - Input any value and retrieve predicted value.
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==== Meteos UI ====
  
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{| border="1" cellpadding="2"
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| Source code
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| https://github.com/openstack/meteos-ui
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|-
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| Bug tracker
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| https://bugs.launchpad.net/meteos-ui
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|-
 +
| Feature tracker
 +
| https://blueprints.launchpad.net/meteos-ui
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|}
  
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=== Archtecture (TBD) ===  
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==== IRC ====
 +
http://webchat.freenode.net/?channels=openstack-meteos
  
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Hibana consist of hibana-api service and hibana-engine service.
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== Design & Use Cases ==
  
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* hibana-api - web service which has REST interface.
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* [[Meteos/Architecture| Meteos Architecture]]
  
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* hibana-engine - service which manage Hibana resources.
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* [[Meteos/Usecases| Meteos Use Cases]]
  
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[[File:Hibana-archtecture.png]]
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* [[Meteos/DatasetsandModels| Meteos Dataset and Prediction Model]]
  
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=== Resource (TBD) ===
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== Getting Started with Meteos ==
  
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* Experiment - a workspace of Machine Learning
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Instructions for getting started with Meteos using Devstack are available at: [[Meteos/Devstack|Meteos on Devstack]]
  
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* Data Set - a data parsed by user to create a Prediction Model
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== API ==
  
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* Prediction Model - a model produced by data mining and machine learning algorithms
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* [[Meteos/API| Meteos API]]
  
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* Learning Job - a job which consists of input data, output data(predicted data), job status, job stdout/stderr.
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== Examples (CLI) ==
  
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=== API (TBD) ===
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* [[Meteos/ExampleLinear| Predict a Sales Figures by using LinearRegression Model]]
  
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==== Experiment ====
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* [[Meteos/ExampleDecisionTree| Make a Decision to buy a stock by using DecisionTree Model]]
  
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* Create Experiment
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* [[Meteos/ExampleKmeans| Classify Users by Skill using Kmeans Model]]
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** POST /v1/<tenant_id>/experiments
 
  
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* List Experiments
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* [[Meteos/ExampleRecommend| Recommend a Movie by using Recommendation Model]]
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** GET /v1/<tenant_id>/experiments
 
  
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* Show Experiment
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* [[Meteos/ExampleWord2Vec| Search Synonyms by using Word2Vec Model]]
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** GET /v1/<tenant_id>/experiments/<experiment_id>
 
  
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* Update Experiment
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== Examples (GUI) ==
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** PUT /v1/<tenant_id>/experiments
 
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* Delete Experiment
 
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** DELETE /v1/<tenant_id>/experiments/<experiment_id>
 
  
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==== Data Set ====
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* [[Meteos/ExampleNativebyes| Detect a Spam Mail using Native byes Model]]
  
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* Create Data Set
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== Tips ==
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** POST /v1/<tenant_id>/datasets
 
  
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* List Data Sets
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* [[Meteos/Howto| How to increase the model accuracy]]
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** GET /v1/<tenant_id>/datasets
 
  
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* Show Data Sets
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== Boston Summit ==
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** GET /v1/<tenant_id>/datasets/<dataset_id>
 
  
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* Update Data Set
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* PPT Slide
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** PUT /v1/<tenant_id>/datasets
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https://www.slideshare.net/guchi_hiro/openstack-meteos-machine-learning-as-a-service
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* Delete Data Set
 
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** DELETE /v1/<tenant_id>/datasets/<dataset_id>
 
  
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==== Data Set Actions====
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* Demo
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https://www.youtube.com/watch?v=YFv6SQvqNFs
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* Export Data Set to Object Storage
 
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** POST /v1/<tenant_id>/datasets/<dataset_id>/action
 
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{"export"}
 
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==== Prediction Model ====
 
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* Create Prediction Model
 
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** POST /v1/<tenant_id>/models
 
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* List Prediction Models
 
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** GET /v1/<tenant_id>/models
 
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* Show Model
 
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** GET /v1/<tenant_id>/models/<moded_id>
 
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* Update Model
 
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** PUT /v1/<tenant_id>/models
 
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* Delete Model
 
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** DELETE /v1/<tenant_id>/models/<model_id>
 
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==== Prediction Model Actions====
 
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* Export Prediction Model
 
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** POST /v1/<tenant_id>/models/<model_id>/action
 
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{"export"}
 
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==== Learning Job ====
 
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* Create Learning Job
 
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** POST /v1/<tenant_id>/jobs
 
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* List Learning Jobs
 
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** GET /v1/<tenant_id>/jobs
 
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* Show Learning Job
 
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** GET /v1/<tenant_id>/jobs/<job_id>
 
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* Delete Learning Job
 
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** DELETE/v1/<tenant_id>/jobs/<job_id>
 

Latest revision as of 04:04, 8 July 2018

Meteos (Machine Learning as a Service) (Deprecated)

(check the new design and mission of Meteos/Meteos2.0)

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).

Meteos-overview.png

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

Meteos UI

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

IRC

http://webchat.freenode.net/?channels=openstack-meteos

Design & Use Cases

Getting Started with Meteos

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

API

Examples (CLI)

Examples (GUI)

Tips

Boston Summit

  • PPT Slide

https://www.slideshare.net/guchi_hiro/openstack-meteos-machine-learning-as-a-service

  • Demo

https://www.youtube.com/watch?v=YFv6SQvqNFs

Retrieved from "https://wiki.openstack.org/w/index.php?title=Meteos&oldid=162583"