Simplify the documentation

README.md

@@ -7,14 +7,9 @@ Disclaimer: The solution and samples provided in this repository is for learning
 
 # Getting Started
 
-[deploy](/deploy/README.md) discusses how to proceed with setting-up the Azure resources required to build/execute the pipeline.
+Start by following the [README.md](./deploy/README.md) to setup the Azure resources required to execute the pipeline.
 
-Sample pipeline provided makes use of the following AI Model:
-
-### a. AI model
-
-This solution uses the [Custom Vision Model](/src/aimodels) as a sample AI model for demonstrating end to end Azure Synapse workflow geospatial analysis. This sample solution uses Custom Vision model to detect pools in a given geospatial data. 
-You can use any other AI model for object detection or otherwise to run against this solution with a similar [specification](/src/aimodels/custom_vision_object_detection_offline/specs/custom_vision_object_detection.json) or different specification as defined by AI model to integrate in your solution.  
+This solution uses the [Custom Vision Model](/src/aimodels) as a sample AI model to demonstrate end-to-end Azure Synapse workflow for geospatial analysis. In this sample solution, the AI model detects swimming pools for a given geospatial data. You can use this solution to integrate other AI models.
 
 ## Contributing
 

deploy/README.md

@@ -1,27 +1,121 @@
 # Overview of Infrastructure deployment and configuration
 
-The deployment involves the following steps outlined below:
+We are using bicep templates to deploy the infrastructure resources.
+We will be using following azure services:
+- Azure Batch Account
+- Azure Synapse Analytics
+- Azure Keyvault
+- Azure Storage Account
+- Azure Networking
+- Azure Log Analytics
 
-No | Step | Duration (approx.) | Required / Optional
----|------|----------|---------------------
-1 | [Preparing to execute the script](./deploy-infrastructure.md#preparing-to-execute-the-script) | 1 minute | required
-2 | [Infrastructure Deployment](./deploy-infrastructure.md#infrastructure-deployment) | 10 minutes | required
-3 | [Verifying infrastructure resources](./deploy-infrastructure.md#verifying-infrastructure-resources) | 5 minutes | required
+# Prerequisites
 
-Follow the [document](./deploy-infrastructure.md) to understand and execute the infrastructure deployment process.
+The deployment script uses following tools, please follow the links provided to install the suggested tools on your computer using which you would execute the script.
 
-## Executing the synapse pipeline
+- [git](https://github.com/git-guides/install-git) 
+- [az cli](https://docs.microsoft.com/cli/azure/install-azure-cli)
+- [jq](https://stedolan.github.io/jq/download/)
+- The scripts are executed on bash shell, so if using a computer with windows based operating system, install a [WSL](https://docs.microsoft.com/windows/wsl/about) environment to execute the script.
+- Install bicep on az cli using command `az bicep install`.
+- The bicep templates have been written to adhere to the syntax and rules for bicep version >= 0.8.9. Please check your bicep version using `az bicep version` if you run into bicep related errors.
+- The user performing the deployment of the bicep template and the associated scripts should have `Owner` role assigned at the subscription to which the resources are being deployed.
+- This solution assumes no interference from Policies deployed to your tenant preventing resources from being deployed. 
+- Get the repository to find the scripts. Clone the repository using following command.
 
-We have developed a sample Azure Synapse Pipeline demostrating the use of custom-vision ai-model to process a sample image.
+```bash
+git clone [email protected]:Azure/Azure-Orbital-Analytics-Samples.git
+```
 
-Follow the [document](./using-pipeline.md#executing-the-pipeline) to configure and execute the pipeline.
+Alternatively, one can use Azure Cloud Bash to deploy this sample solution in their Azure subscription.
+
+## Infrastructure Deployment
+
+Before executing the script one would need to login to azure using `az` cli and set the subscription in which they want to provision the resources.
+
+```bash
+az login
+az account set -s <subscription_id>
+```
+
+To install, configure and package the infrastructure for dependencies one can execute the script [setup.sh](./setup.sh)
+
+Script has been written to be executed with minimalistic input, it requires following input
+- `environmentCode` which serves as the prefix for infrastructure services names. Allows only alpha numeric(no special characters) and must be between 3 and 8 characters.
+- `location` which suggests which azure region infrastructure is deployed in.
+
+```bash
+./deploy/setup.sh <environmentCode> <location>
+```
+
+For eg.
+```bash
+./deploy/setup.sh aoi eastus
+```
+
+[setup.sh](./setup.sh) executes tasks in 3 steps
+- installs the infrastructure using [install.sh](./install.sh) script.
+- configures the infrastructure for setting up the dependecies using [configure.sh](./configure.sh) script.
+- packages the pipeline code to imported into synapse for standalone executions using [package.sh](./package.sh) script.
+
+**Note**: After the script is completed without errors, please check the batch-account pool created is created succesfully.
+
+One can find the batch account named as `batchact<10-character-random-string>` in the resource-group `<environment-code>-orc-rg`.
+
+Go to the Batch Account and switch to the pools blade. Look for one or more pools created by the bicep template. Make sure the resizing of the pool is completed without any errors. 
+
+- Error while resizing the pools are indicated by red exclamation icon next to the pool. Most common issues causing failure are related to the VM Quota limitations.
+- Resizing may take a few minutes. Pools that are resizing are indicated by `0 -> 1` numbers under dedicated nodes column. Pools that have completed resizing should show the number of dedicated nodes. 
+
+Wait for all pools to complete resizing before moving to the next steps.
+
+## Executing Synapse Pipeline
+
+We have published the Pipeline on Synapse gallery, follow the following steps to find the Synapse pipeline and execute.
+
+- Find your Synapse workspace named as `synws<10-character-random-string>` in resource group `<environment-code>-pipeline-rg`.
+- Click on `Workspace web URL` to reach the synapse workspace portal homepage.
+- Click on `Knowledge center`.
+- Click on `Browse gallery`.
+- Click on `Pipelines` in the top menu.
+- In the search box type `space` to find the pipeline named `Spaceborne Data Analysis Master Pipeline`.
+- Click on pipeline name to select it, and press `Continue` button at the end of the page.
+
+Follow the steps in [instructions.md](./gallery/instructions.md) to configure and execute the pipeline.
+
+## Cleanup Script
+
+We have a cleanup script to cleanup the resource groups and thus the resources provisioned using the `environmentCode`.
+As discussed above the `environmentCode` is used as prefix to generate resource group names, so the cleanup-script deletes the resource groups with generated names.
+
+Execute the cleanup script as follows:
+
+```bash
+./deploy/cleanup.sh <environmentCode>
+```
+
+For eg.
+```bash
+./deploy/cleanup.sh aoi
+```
+
+If one wants not to delete any specific resource group and thus resource they can use NO_DELETE_*_RESOURCE_GROUP environment variable, by setting it to true
+
+```bash
+NO_DELETE_DATA_RESOURCE_GROUP=true
+NO_DELETE_MONITORING_RESOURCE_GROUP=true
+NO_DELETE_NETWORKING_RESOURCE_GROUP=true
+NO_DELETE_ORCHESTRATION_RESOURCE_GROUP=true
+NO_DELETE_PIPELINE_RESOURCE_GROUP=true
+./deploy/cleanup.sh <environmentCode>
+```
 
 ## AI Model
 
 This solution uses the [Custom Vision Model](/src/aimodels) as a sample AI model for demonstrating end to end Azure Synapse workflow geospatial analysis.
 
 This sample solution uses Custom Vision model to detect pools in a given geospatial data. 
 
-You can use any other AI model for object detection or otherwise to run against this solution with a similar [specification](/src/aimodels/custom_vision_object_detection_offline/specs/custom_vision_object_detection.json) or different specification as defined by AI model to integrate in your solution.
+You can use other AI model for object detection or otherwise to run against this solution with a similar [specification](/src/aimodels/custom_vision_object_detection_offline/specs/custom_vision_object_detection.json) or different specification as defined by AI model to integrate in your solution.
 
 Follow the [document](./bring-your-own-ai-model.md) to understand and use a different ai-model for processing with the pipeline.