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StartBaseScenario.md

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Run an end-to-end MLOps pipeline

Section 1: Setup Github & Integration with Devops

  1. Perform the initial setup if not already done
  2. Fork the MLOps repository
    1. Visit the MLOps github project and click Fork on top right.
    2. Give a name & let it remain "Public" for course of this workshop
    3. In your project page copy your clone https url by clicking on the Clone or Download button - you will need this for next step
  3. Setup integration between Devops & Github
    1. In the Azure Devops project you created earlier goto: Pipelines -> Pipelines
    2. Create new pipeline by clicking "New pipeline" button on top right
    3. To respond to question "Where is your code", click Github (YAML)
      1. Authorize the two requests: One for Azure devops to connect to github to load your pipelines & another for integration of triggers from github to Devops
      2. Click Existing Azure Pipelines YAML file
      3. Now you will see a list of YML (yaml) files. Follow next step below.

Section 2: Create the environment

This step will create the cloud environment and provision all the required services including Resource group & Azure ML Workspace

  1. Continuing from the last section: select mlops_pipelines/EnvCreatePipeline.yml -> Click Continue -> Click Run
  2. You can monitor the status by clicking on the stages. Check if your pipeline ran successfully.

Section 3: Create a Dataset in Azure ML

  1. Login into the Azure ML portal and switch to the new workspace that was created using the build pipeline:
    1. In the top nav bar, click the Switch Directory icon
    2. Click on the drop down Machine learning workspace and select the one that was just created (BASE_NAME-aml-ws)
  2. In the left panel click Dataset -> Create Dataset -> from web files
    1. Paste this url https://raw.githubusercontent.com/rsethur/MLOps/master/dataset/german_credit_data.csv
    2. Important Change dataset name to credit_dataset
    3. Follow the onscreen instructions by clicking Next till completion

Section 4: Run the Build Release pipeline

Now we can run training & automate the deployment by running the build release pipeline.

  1. Similar to first pipeline we ran, in the Azure Devops projectgoto: Pipelines -> Pipelines
  2. Create new pipeline by clicking "New pipeline" button on top right
  3. To respond to question "Where is your code", click Github (YAML)
    1. Click Existing Azure Pipelines YAML file
    2. Now you will see a list of YML (yaml) files: select mlops_pipelines/BuildReleasePipeline.yml -> Click Continue -> Click Run

You can monitor the status by clicking on the stages. Check if your pipeline ran successfully.

Section 5: Test the deployed service

  1. Get the service endpoint:
    1. Navigate to the Azure ML portal
    2. Go to Endpoints in the left nav bar -> click on the deployed service
    3. copy the url of the REST endpoint from the details
  2. Two options for testing the end point
    1. Easy option:
      1. Go to the prefilled request details in this page
      2. Replace the existing URL with the new endpoint url -> click the Test button
      3. You should see [0] in the response body (The label 0 means bad and 1 means good from this credit risk model's perspective). For this data you will see a 0.
    2. Another option:
      1. Use your favourite REST endpoint testing tool like Postman or API Tester (online)
      2. Fill the details:
        1. Your rest endpoint URL
        2. Set content-type to application/json
        3. Request type to POST
        4. Copy the following post data -> press submit/test
        {
  'data': {
    "Age": [
      20
    ],
    "Sex": [
      "male"
    ],
    "Job": [
      0
    ],
    "Housing": [
      "own"
    ],
    "Saving accounts": [
      "little"
    ],
    "Checking account": [
      "little"
    ],
    "Credit amount": [
      100
    ],
    "Duration": [
      48
    ],
    "Purpose": [
      "radio/TV"
    ]
  }
}

Section 6: Clean up

Once you understand the concepts & ready to delete the resources you can fo the the following:

From Azure Devops run the pipeline mlops_pipelines/EnvTearDownPipeline.yml. Alternatively you can login to Azure Portal and delete the resource group that we created (i.e. your-unique-name-aml-rg).

Do ⭐ the repo if you like it.