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Hibana (Machine Learning as a Service)

Hibana is Machine Learning as a Service (MLaaS) in Apache Spark.

Projects

Hibana

Python Hibana Client

Use Cases

Machine Learning consists of the following phases.

• Learning Phase - Analyze huge amounts of data and create a Prediction Model
• Prediction Phase - Predict a value according to the input value by using Prediction Model

Use Cases in Learning Phase

• Upload Raw Data - Upload a raw data to Object Stroage
• 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.
• 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.

Resource (TBD)

• 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>