adding Azure Machine Learning lab (#183)

* adding Azure Machine Learning lab

* updated content

* updated content

labs/vstsextend/aml/Readme.md

@@ -0,0 +1,126 @@
+---
+title: Enabling CI/CD for Machine Learning project with Azure Pipelines
+layout: page
+sidebar: vsts2
+permalink: /labs/vstsextend/aml/
+folder: /labs/vstsextend/aml/
+---
+## Overview
+**Machine learning** is a data science technique that allows computers to use existing data to forecast future behaviours, outcomes, and trends. By using machine learning, computers learn without being explicitly programmed.
+
+**Azure Machine Learning service** provides a cloud-based environment you can use to develop, train, test, deploy, manage, and track machine learning models. 
+
+![](images/aml.png)
+
+### What’s covered in this lab
+In this lab, you will see
+1. How to build the Continuous Integration and Continuous Delivery pipelines for a Machine Learning project with **Azure Pipelines**. 
+
+We will be using the Azure DevOps project for build and release pipelines along with Azure ML services for ML/AI model management and operationalization. 
+
+This template contains code and pipeline definition for a machine learning project demonstrating how to automate the end to end ML/AI project. The build pipelines include DevOps tasks for data sanity test, model training on different compute targets, model version management, model evaluation/model selection, model deployment as real-time web service, staged deployment to QA/prod, integration testing and functional testing.
+
+### Before you begin
+
+1. Refer the [Getting Started](../Setup/) page before you begin following the exercises.
+
+1. Use the [Azure DevOps Demo Generator](https://azuredevopsdemogenerator.azurewebsites.net/?name=azure%20machine%20learning) to provision the project on your Azure DevOps organization.
+   This URL will automatically select **Azure Machine Learning** template in the demo generator. This template contains code and pipeline definition for a machine learning project demonstrating how to automate the end to end ML/AI project.
+   
+    If you want to try other projects, use this URL instead -https://azuredevopsdemogenerator.azurewebsites.net/
+
+   Follow the [simple walkthrough](https://docs.microsoft.com/en-us/azure/devops/demo-gen/use-vsts-demo-generator-v2?view=vsts) to know how to use the Azure DevOps Demo Generator.
+
+### Exercise 1: Configure CI pipeline for ML/AI project
+
+In this exercise, you will configure CI pipeline for your ML/AI project. This build pipeline includes DevOps tasks for data sanity test, model training on different compute targets, model version management, model evaluation/model selection etc.
+
+1.  Navigate to **Pipeline > Builds**. Select **DevOps-for-AI-CI** and click **Edit**.
+
+     ![](images/edit-ci.png)
+
+1. Your build pipeline will look like as below. 
+   
+     ![](images/ci-pipeline.png)
+
+     In this pipeline [Conda Environment](https://docs.microsoft.com/en-us/azure/devops/pipelines/tasks/package/conda-environment?view=azdevops) task and **Install requirements** task are used to setup and prepare the Python environment to use it for subsequent build tasks.
+
+1. Select **Create or Get Workspace** task. Select the Azure subscription from the drop-down list and click **Authorize** to configure Azure service connection. This task used here to create [Workspace](https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-azure-machine-learning-architecture#workspace) for Azure Machine learning service.
+
+    ![](images/create-workspace.png)
+
+ 1. Click all other tasks in the pipeline and select the same subscription. Once the tasks are updated with a subscription, **Save** the changes.
+     
+       ![](images/save-ci.png)
+
+1. Select **Triggers** and make sure that CI is enabled.
+      
+      ![](images/ci-trigger.png)
+
+1. The steps performed in the CI pipeline are 
+
+    - Prepare the python environment
+    - Get or Create the workspace for AML service
+    - Submit Training job on the remote DSVM / Local Python Env
+    - Compare performance of different models and select the best
+    - Register model to the workspace
+    - Create Docker Image for Scoring Web service
+    - Copy and Publish the Artifacts to Release Pipeline
+
+### Exercise 2: Configure CD pipeline for ML/AI project
+In this exercise, we will configure Release pipeline which will deploy the image created from the build pipeline to Azure Container Instance and Azure Kubernetes Services
+
+1. Navigate to **Pipeline >> Releases**. Select **Deploy Web service** and click **Edit** pipeline. 
+
+   ![](images/edit-cd.png)
+
+1. You will see release pipeline with two stages **QA** and **Prod**. Select **QA** stage and select **view stage tasks** to view the tasks in **QA** stage.
+    
+    ![](images/qa-stage.png)
+
+1. In **QA** stage [Conda Environment](https://docs.microsoft.com/en-us/azure/devops/pipelines/tasks/package/conda-environment?view=azdevops) task and **Install requirements** tasks are used to setup and prepare the Python environment to use it for subsequent tasks.
+       ![](images/qa-env.png)
+
+1. Select **Deploy Web service on ACI** task. Select Azure subscription details. This task creates ACI ([Azure Container Instance](https://azure.microsoft.com/en-in/services/container-instances/)) and deploys web service image created in Build Pipeline to ACI.
+
+    ![](images/qa-aci.png)
+
+1. Select **Test ACI Web service** task. Update the Azure subscription details. This task is to test the scoring image deployed.
+
+   ![](images/qa-test.png)
+
+1. Click on **Tasks** on the top to switch to the **Prod** stage, update the subscription details for the two tasks in prod stage and click **Save** to save the changes.
+
+    ![](images/prod-stage.png)
+
+    ![](images/prod-tasks.png)
+
+### Exercise 3: Update config file in the source code to trigger CI and CD
+
+1. Navigate to **Repos**. Open **config.json** file from the folder **aml_config/**.
+
+      ![](images/config-edit.png)
+
+1. Update your Azure subscription ID in place of <>. If  required Change resource group name, AML workspace name and the location where you want to deploy your Azure ML service workspace. Click **Commit** to commit the changes.
+
+      ![](images/update-config.png)
+
+   > You can find the details of the code and scripts [here](https://github.com/praneetmsft/DevOpsForAI/blob/master/docs/code_description.md)
+
+1. Since we have enabled in **CI trigger** a build will be queued automatically. Navigate to **Pipelines -->Builds** and you will see a build is queued. Open the build to see the progress.
+Once the build is success a release will be queued automatically. Navigate to **Pipelines --> Releases** to see the release in progress.
+  
+   ![](images/build-release-progress.gif)
+
+1. Now navigate to your Azure portal to view the resources provisioned and deployed by CI-CD pipelines.
+   
+    ![](images/azureportal1.gif)
+
+
+### References 
+Do you want to learn more about **Azure Machine Learning service**? if yes below are few references to look at
+- [Azure Machine Learning(Azure ML) Service Workspace](https://docs.microsoft.com/en-us/azure/machine-learning/service/overview-what-is-azure-ml)
+
+- [Azure ML Samples](https://docs.microsoft.com/en-us/azure/machine-learning/service/samples-notebooks)
+- [Azure ML Python SDK Quickstart](https://docs.microsoft.com/en-us/azure/machine-learning/service/quickstart-create-workspace-with-python)
+- [Azure DevOps](https://docs.microsoft.com/en-us/azure/devops/?view=vsts)

labs/vstsextend/ansible/readme.md

@@ -24,7 +24,7 @@ In this lab, you will see
 
 ### Before you begin
 
-1. Refer the [Getting Started](https://github.com/Hoyg/azuredevopslabs/blob/master/labs/vstsextend/Setup) page before you begin following the exercises.
+1. Refer the [Getting Started](../Setup/) page before you begin following the exercises.
 
 1. Use the [Azure DevOps Demo Generator](https://azuredevopsdemogenerator.azurewebsites.net/?Name=Ansible) to provision the project on your Azure DevOps organization.
    This URL will automatically select **Ansible** template in the demo generator. If you want to try other projects, use this URL instead -https://azuredevopsdemogenerator.azurewebsites.net/

readme.md

@@ -175,6 +175,9 @@ description: Learn how you can implement modern DevOps practices with Azure, Azu
                                                 <li class="osslist"> <img src="/images/ansible.png" height="42" class="icon-bor-p-m" /><a
                                                         href="/labs/vstsextend/ansible/" class="fon-size-13">
                                                         Automating your infrastructure deployments in the Cloud with Ansible and Azure Pipelines</a></li>
+                                                <li class="osslist"> <img src="/images/aml.png" height="42" class="icon-bor-p-m" /><a
+                                                        href="/labs/vstsextend/aml/" class="fon-size-13">
+                                                        Enabling CI/CD for Machine Learning project with Azure Pipelines</a></li>
                                                 <li class="osslist"> <img src="/images/github-42.png" height="42" class="icon-bor-p-m" /><a
                                                         href="/labs/azuredevops/github-integration/" class="fon-size-13">
                                                         Setting up a CI/CD pipeline for your GitHub repository</a></li>