.project-metadata.yaml

name: CML Fraud Demo
description: Prototype to demonstrate use of ML to identify fraudulent transactions
author: Cloudera Engineer
specification_version: 1.0
prototype_version: 1.0
date: "2020-09-29"
api_version: 1

tasks:
  - type: create_job
    name: Install dependencies
    entity_label: install_deps
    script: 0_bootstrap.py
    arguments: None
    cpu: 1
    memory: 4
    short_summary: Job to install dependencies and download training data.
    environment:
      TASK_TYPE: CREATE/RUN_JOB
    kernel: python3

  - type: run_job
    entity_label: install_deps
    short_summary: Running install dependencies training job.
    long_summary: >-
      Running the job to install dependencies.

  # - type: create_job
  #   name: Train Fraud Model
  #   entity_label: train_model
  #   script: 3_model_train.py
  #   arguments: None
  #   cpu: 1
  #   memory: 4
  #   short_summary: Job to train model.
  #   environment:
  #     TASK_TYPE: CREATE/RUN_JOB
  #   kernel: python3

  # - type: run_job
  #   entity_label: train_model
  #   short_summary: Run model train job.
  #   long_summary: Running job to train model.

  - type: create_model
    name: Create Fraud Model API Endpoint
    entity_label: fraud_model
    description: This model API endpoint predicts fraud
    short_summary: Create the fraud model prediction API endpoint
    access_key_environment_variable: SHTM_ACCESS_KEY
    default_resources:
      cpu: 1
      memory: 2
    default_replication_policy:
      type: fixed
      num_replicas: 1

  - type: build_model
    name: Build Fraud Model Endpoint
    entity_label: fraud_model
    comment: Build churn model
    examples:
      - request:
          {
            "v":
              [
                -1.3598071336738,
                -0.0727811733098497,
                2.53634673796914,
                1.37815522427443,
                -0.338320769942518,
                0.462387777762292,
                0.239598554061257,
                0.0986979012610507,
                0.363786969611213,
                0.0907941719789316,
                -0.551599533260813,
                -0.617800855762348,
                -0.991389847235408,
                -0.311169353699879,
                1.46817697209427,
                -0.470400525259478,
                0.207971241929242,
                0.0257905801985591,
                0.403992960255733,
                0.251412098239705,
                -0.018306777944153,
                0.277837575558899,
                -0.110473910188767,
                0.0669280749146731,
                0.128539358273528,
                -0.189114843888824,
                0.133558376740387,
                -0.0210530534538215,
              ],
            "time": 0,
            "amount": 149.62,
          }
        response: ""
    target_file_path: 4_model_deploy.py
    target_function_name: predict
    kernel: python3
    environment_variables:
      TASK_TYPE: CREATE/BUILD/DEPLOY_MODEL

  - type: deploy_model
    name: fraud_model
    entity_label: fraud_model
    cpu: 1
    gpu: 0
    environment_variables:
      TASK_TYPE: CREATE/BUILD/DEPLOY_MODEL

  - type: start_application
    name: Application to serve Fraud front app UI
    subdomain: fraud
    script: 5_application.py
    environment_variables:
      TASK_TYPE: START_APPLICATION
    kernel: python3