docs: updated readme

Author: praneet22

README.md

@@ -1,3 +1,94 @@
+### Author: Praneet Singh Solanki
+
+# DevOps For AI
+
+[DevOps for AI template](https://azuredevopsdemogenerator.azurewebsites.net/?name=azure%20machine%20learning) will help you to understand how to build the Continuous Integration and Continuous Delivery pipeline for a ML/AI project. 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, unit test, model training on different compute targets, model version management, model evaluation/model selection, model deployment as realtime web service, staged deployment to QA/prod, integration testing and functional testing.
+
+## Prerequisite
+- Active Azure subscription
+- Minimum contributor access to Azure subscription
+
+## Getting Started:
+
+### Import the DevOps for AI solution template from Azure DevOps Demo Generator: [Click here](https://azuredevopsdemogenerator.azurewebsites.net/?name=azure%20machine%20learning)
+
+Skip above step if already done.
+
+Once the template is imported for personal Azure DevOps account using DevOps demo generator, you need to follow below steps to get the pipeline running:
+
+### Update Pipeline Config:
+
+#### Build Pipeline
+1. Go to the **Pipelines -> Builds** on the newly created project and click **Edit** on top right
+![EditPipeline1](/docs/images/EditPipeline1.png)
+2. Click on **Create or Get Workspace** task, select the Azure subscription where you want to deploy and run the solution, and click **Authorize**
+![EditPipeline2](/docs/images/EditPipeline2.png)
+3. Click all other tasks below it and select the same subscription (no need to authorize again)
+4. Once the tasks are updated with subscription, click on **Save & queue** and select **Save**
+![EditPipeline3](/docs/images/EditPipeline3.png)
+
+#### Release Pipeline
+1. Go to the **Pipelines -> Releases** and click **Edit** on top  
+![EditPipeline4](/docs/images/EditPipeline4.png)
+2. Click on **1 job, 4 tasks** to open the tasks in **QA stage**
+![EditPipeline5](/docs/images/EditPipeline5.png)
+3. Update the subscription details in two tasks 
+![EditPipeline6](/docs/images/EditPipeline6.png)
+4. Click on **Tasks** on the top to switch to the Prod stage, update the subscription details for the two tasks in prod
+![EditPipeline7](/docs/images/EditPipeline7.png)
+5. Once you fix all the missing subscription, the **Save** is no longer grayed, click on save to save the changes in release pepeline
+![EditPipeline8](/docs/images/EditPipeline8.png)
+
+### Update Repo config:
+1. Go to the **Repos** on the newly created Azure DevOps project
+2. Open the config file [/aml_config/config.json](/aml_config/config.json) and edit it
+3. Put your Azure subscription ID in place of <>
+4. Change resource group and AML workspace name if you want
+5. Put the location where you want to deploy your Azure ML service workspace
+6. Save the changes and commit these changes to master branch 
+7. The commit will trigger the build pipeline to run deploying AML end to end solution
+8. Go to **Pipelines -> Builds** to see the pipeline run
+
+## Steps Performed in the Build Pipeline:
+
+1. Prepare the python environment
+2. Get or Create the workspace
+3. Submit Training job on the remote DSVM / Local Python Env
+4. Register model to workspace
+5. Create Docker Image for Scoring Webservice
+6. Copy and Publish the Artifacts to Release Pipeline
+
+## Steps Performed in the Release Pipeline
+In Release pipeline we deploy the image created from the build pipeline to Azure Container Instance and Azure Kubernetes Services
+
+### Deploy on ACI - QA Stage
+1. Prepare the python environment
+2. Create ACI and Deploy webservice image created in Build Pipeline
+3. Test the scoring image
+
+### Deploy on AKS - PreProd/Prod Stage
+1. Prepare the python environment
+2. Deploy on AKS
+    - Create AKS and create a new webservice on AKS with the scoring docker image
+
+        OR
+
+    - Get the existing AKS and update the webservice with new image created in Build Pipeline
+3. Test the scoring image
+
+### Repo Details
+
+You can find the details of the code ans scripts in the repository [here](/docs/code_description.md)
+
+### References
+
+- [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)
 
 # Contributing