getting_started.md

Getting Started

Setup and Deploy Platform

1. Create a new GitHub repository

1. Login to your GitHub account, navigate to the AML Real-Time Deployment Template repository and create a new repository from this template. Use these instructions for more details about creating a repository from a template.

2. Create and configure a new Azure DevOps Project

1. Create a new project in Azure DevOps. Use these instructions for more details about creating a project in Azure DevOps.

2. Create an Azure Resource Manager Service connection. This is needed so Azure Devops can connect to your subscription and create/manage resources.

   1. Click on project settings (found at the bottom-left of the portal).
   2. Click on Service Connections (found on the sidebar).
   3. Click New Service Connection (found at the top-right of the portal).
   4. Select Azure Resource Manager and click Next.
   5. Select Service principal (automatic) and click Next.
   6. Leave Subscription as the scope level and select your subscription from the dropdown.
   7. In the Service connection name textbox enter azure-service-connection. Note: you can enter another name for your service connection but this will require editing the variables.yml file.
   8. Leave the Allow all pipelines to use this connection checkbox selected and click Next.

   The above steps assume you have Contributor or Owner access to the subscription or resource group.

   See these instructions for more details about creating an Azure Resource Manager Service connection.

3. Create a variable group in Azure DevOps to store values that are reused across multiple pipelines or pipeline stages.

   1. Select the Library tab from the Pipelines section (found on the sidebar).

   2. Click + Variable group and create a variable group named aml-deployment-templates. Note: you can enter another name for your variable group but this will require editing the variables.yml file.

   3. The variable group should contain the following required variables:

      Variable Name	Suggested Value
      aks_cluster_name	aks-cluster
      aml_sku	enterprise
      compute_cluster_name	cpu-cluster
      environment	development
      location	australiaeast
      namespace	amlplatform
      service_connection	azure-service-connection

See these instructions for more details about creating a variable group in Azure DevOps.

3. Setup the environment

1. The easiest way to setup the environment is by using the AML Platform Deployment Template.

2. Alternatively, you can provision the infrastructure manually by:

   1. Creating an Azure Machine Learning Workspace
   2. Creating a compute cluster with the same name as the compute_cluster_name variable in the variable group in Azure DevOps.
   3. Creating an inference cluster with the same name as the aks_cluster_name variable in the variable group in Azure DevOps. This will create an AKS cluster in your resource group.
   4. Registering a dataset with the name cardiovascular_disease_dataset using the Cardiovascular Disease dataset (which has been adapted from Kaggle) and is available here.

   The above steps assume you have Contributor or Owner access to the subscription or resource group.

4. Deploy the solution

1. In your GitHub repository, edit any variables in the variables.yml you wish to change for your deployment. Remember to change the values for service_connection or resource_group_name if you have selected something different in section 2 step 2 above.

2. In your Azure DevOps project, create a pipeline from your repository.

   1. Select the Pipelines tab from the Pipelines section (found on the sidebar).
   2. Click on New Pipeline (found at the top-right of the portal).
   3. Select GitHub (authenticate if necessary).
   4. Select your GitHub repository from the list of repositories (authenticate if necessary).
   5. Select Existing Azure Pipelines YAML File.
   6. Select the master branch and enter /.pipeline/build-release.yml as the path or select it from the dropdown and click Continue.
   7. Select Run from the top-right of the protal to execute the pipeline and deploy the platform.

   See these instructions for more details about creating a pipeline in Azure DevOps.

5. Use data drift

1. When you call your web service endpoint all telemetry is collected and stored in the workspace storage account in a container called modeldata. The path to the inputs and predictions output data in the blob follows this syntax: /modeldata/<subscriptionid>/<resourcegroup>/<workspace>/<webservice>/<model>/<version>/<designation>/<year>/<month>/<day>/data.csv. Read more information here.

2. This stored telemetry will be accessable via a dataset in the following form: inference-data-<model_name>-<model_version>-<endpoint_name>. A data drift monitor will be configured using this dataset as a target data and the training dataset as a baseline. This means the web service telemetry will be compared against the training (baseline) dataset to determine if model drift is present.

3. The model data drift detector (as configured in this template) will automatically execute every day to determine if drift is present. Read more about model drift here.