Difference between revisions of "Meteos"

Revision as of 02:18, 29 November 2016

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

Python Meteos Client

Getting Started with Meteos

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

Design & Use Cases

• Meteos Architecture

• Meteos Use Cases

• Meteos Resources

• Meteos Prediction Models

API

• Meteos API

Examples

• Predict Sales by using LinearRegression Model

• Predict Victory or Defeat by using LogisticRegression Model

• Make a Decision by using DecisionTree Model

• Clustering User Preferences by using Kmeans Model

• Recommend Movie by using Recommendation Model

Resource (TBD)

• Experiment Template - Template which define experiment (e.g. number of master/worker nodes, spark version, base VM image, 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
  • GET /v1/<tenant_id>/experiments/<experiment_id>

• Update Experiment
  • PUT /v1/<tenant_id>/experiments

• Delete Experiment
  • DELETE /v1/<tenant_id>/experiments/<experiment_id>

Data Set

• Create Data Set
  • POST /v1/<tenant_id>/datasets

• List Data Sets
  • GET /v1/<tenant_id>/datasets

• Show Data Sets
  • GET /v1/<tenant_id>/datasets/<dataset_id>

• Update Data Set
  • PUT /v1/<tenant_id>/datasets

• Delete Data Set
  • DELETE /v1/<tenant_id>/datasets/<dataset_id>

Data Set Actions

• Export Data Set to Object Storage
  • POST /v1/<tenant_id>/datasets/<dataset_id>/action
    • BODY {"export"}

Prediction Model

• Create Prediction Model
  • POST /v1/<tenant_id>/models

• List Prediction Models
  • GET /v1/<tenant_id>/models

• Show Model
  • GET /v1/<tenant_id>/models/<moded_id>

• Update Model
  • PUT /v1/<tenant_id>/models

• Delete Model
  • DELETE /v1/<tenant_id>/models/<model_id>

Prediction Model Actions

• Export Prediction Model to Object Storage
  • POST /v1/<tenant_id>/models/<model_id>/action
    • BODY {"export"}

Learning Job

• Create Learning Job
  • POST /v1/<tenant_id>/jobs

• List Learning Jobs
  • GET /v1/<tenant_id>/jobs

• Show Learning Job
  • GET /v1/<tenant_id>/jobs/<job_id>

• Delete Learning Job
  • DELETE/v1/<tenant_id>/jobs/<job_id>