Difference between revisions of "Meteos"

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).

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

Meteos Architecture

Meteos Use Cases

Meteos Dataset and Prediction Model

Getting Started with Meteos

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

API

Meteos API

Examples (CLI)

Predict a Sales Figures by using LinearRegression Model

Make a Decision to buy a stock by using DecisionTree Model

Classify Users by Skill using Kmeans Model

Recommend a Movie by using Recommendation Model

Search Synonyms by using Word2Vec Model

Examples (GUI)

Detect a Spam Mail using Native byes Model

Tips

How to increase the model accuracy

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

@@ Line 1: / Line 1: @@
-== Hibana (Machine Learning as a Service) ==
+== Meteos (Machine Learning as a Service) (Deprecated) ==
-Hibana is Machine Learning as a Service (MLaaS) in Apache Spark.
+(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).
+[[File:Meteos-overview.png]]
 == Projects ==
-=== Hibana ===
+==== Meteos ====
 {| border="1" cellpadding="2"
 | Source code
-| Not Yet
+| https://github.com/openstack/meteos
 |-
 | Bug tracker
-| https://bugs.launchpad.net/hibana
+| https://bugs.launchpad.net/meteos
 |-
 | Feature tracker
-| https://blueprints.launchpad.net/hibana
+| https://blueprints.launchpad.net/meteos
 |}
-=== Python Hibana Client ===
+==== Python Meteos Client ====
 {| border="1" cellpadding="2"
 | Source code
-| Not Yet
+| https://github.com/openstack/python-meteosclient
 |-
 | Bug tracker
-| https://bugs.launchpad.net/python-hibanaclient
+| https://bugs.launchpad.net/python-meteosclient
 |-
 | Feature tracker
-| https://blueprints.launchpad.net/python-hibanaclient
+| https://blueprints.launchpad.net/python-meteosclient
 |}
-== Use Cases ==
+==== Meteos UI ====
-Machine Learning consists of the following phases.
+{| border="1" cellpadding="2"
+| Source code
-* Learning Phase - Analyze huge amounts of data and create a Prediction Model
+| https://github.com/openstack/meteos-ui
-* Prediction Phase - Predict a value according to the input value by using Prediction Model
+|-
+| Bug tracker
-=== Use Cases in Learning Phase ===
+| https://bugs.launchpad.net/meteos-ui
+|-
-* Upload Raw Data - Upload a raw data to Object Stroage
+| Feature tracker
-* 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.
+| https://blueprints.launchpad.net/meteos-ui
-* Create Prediction Model - Create a Prediction Model by using MLlib. This Model supports model export to Predictive Model Markup Language (PMML).
+|}
-=== Use Cases in Prediction Phase ===
-* Predict - Input any value and retrieve predicted value.
-== Archtecture (TBD) ==
-Hibana consist of hibana-api service and hibana-engine service.
-* hibana-api - web service which has REST interface.
-* hibana-engine - service which manage Hibana resources.
-[[File:Hibana-archtecture.png]]
-== Resource (TBD) ==
-* Experiment Template - Template which define experiment (e.g. number of master/worker nodes, spark version, flavor, network, ...)
-* Experiment - a workspace of Machine Learning
-* Data Set - a data parsed by user to create a Prediction Model
-* Prediction Model - a model produced by data mining and machine learning algorithms
-* Learning Job - a job which consists of input data, output data(predicted data), job status, job stdout/stderr.
-== API (TBD) ==
-=== Experiment Template ===
-* Create Experiment Template
-** POST /v1/<tenant_id>/templates
-* List Experiment Templates
-** GET /v1/<tenant_id>/templates
-* Show Experiment Template
-** GET /v1/<tenant_id>/templates/<template_id>
-* Update Experiment Template
-** PUT /v1/<tenant_id>/templates
-* Delete Experiment Template
-** DELETE /v1/<tenant_id>/templates/<template_id>
-=== Experiment ===
-* Create Experiment
-** POST /v1/<tenant_id>/experiments
-* List Experiments
-** GET /v1/<tenant_id>/experiments
-* Show Experiment
+==== IRC ====
-** GET /v1/<tenant_id>/experiments/<experiment_id>
+http://webchat.freenode.net/?channels=openstack-meteos
-* Update Experiment
+== Design & Use Cases ==
-** PUT /v1/<tenant_id>/experiments
-* Delete Experiment
-** DELETE /v1/<tenant_id>/experiments/<experiment_id>
-=== Data Set ===
+* [[Meteos/Architecture| Meteos Architecture]]
-* Create Data Set
+* [[Meteos/Usecases| Meteos Use Cases]]
-** POST /v1/<tenant_id>/datasets
-* List Data Sets
+* [[Meteos/DatasetsandModels| Meteos Dataset and Prediction Model]]
-** GET /v1/<tenant_id>/datasets
-* Show Data Sets
+== Getting Started with Meteos ==
-** GET /v1/<tenant_id>/datasets/<dataset_id>
-* Update Data Set
+Instructions for getting started with Meteos using Devstack are available at: [[Meteos/Devstack|Meteos on Devstack]]
-** PUT /v1/<tenant_id>/datasets
-* Delete Data Set
-** DELETE /v1/<tenant_id>/datasets/<dataset_id>
-==== Data Set Actions====
+== API ==
-* Export Data Set to Object Storage
+* [[Meteos/API| Meteos API]]
-** POST /v1/<tenant_id>/datasets/<dataset_id>/action
-*** BODY {"export"}
-=== Prediction Model ===
+== Examples (CLI) ==
-* Create Prediction Model
+* [[Meteos/ExampleLinear| Predict a Sales Figures by using LinearRegression Model]]
-** POST /v1/<tenant_id>/models
-* List Prediction Models
+* [[Meteos/ExampleDecisionTree| Make a Decision to buy a stock by using DecisionTree Model]]
-** GET /v1/<tenant_id>/models
-* Show Model
+* [[Meteos/ExampleKmeans| Classify Users by Skill using Kmeans Model]]
-** GET /v1/<tenant_id>/models/<moded_id>
-* Update Model
+* [[Meteos/ExampleRecommend| Recommend a Movie by using Recommendation Model]]
-** PUT /v1/<tenant_id>/models
-* Delete Model
+* [[Meteos/ExampleWord2Vec| Search Synonyms by using Word2Vec Model]]
-** DELETE /v1/<tenant_id>/models/<model_id>
-==== Prediction Model Actions====
+== Examples (GUI) ==
-* Export Prediction Model to Object Storage
+* [[Meteos/ExampleNativebyes| Detect a Spam Mail using Native byes Model]]
-** POST /v1/<tenant_id>/models/<model_id>/action
-*** BODY {"export"}
-=== Learning Job ===
+== Tips ==
-* Create Learning Job
+* [[Meteos/Howto| How to increase the model accuracy]]
-** POST /v1/<tenant_id>/jobs
-* List Learning Jobs
+== Boston Summit ==
-** GET /v1/<tenant_id>/jobs
-* Show Learning Job
+* PPT Slide
-** GET /v1/<tenant_id>/jobs/<job_id>
+https://www.slideshare.net/guchi_hiro/openstack-meteos-machine-learning-as-a-service
-* Delete Learning Job
+* Demo
-** DELETE/v1/<tenant_id>/jobs/<job_id>
+https://www.youtube.com/watch?v=YFv6SQvqNFs