This template creates an Azure Machine Learning service workspace.
If you are new to Azure Machine Learning, see:
- Azure Machine Learning service
- Azure Machine Learning documentation
- Azure Machine Learning template reference
- Quickstart templates
If you are new to template development, see:
- Azure Resource Manager documentation
- Create an Azure Machine Learning service workspace by using a template
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subscription id: You can get this by going to <azure.portal.com> and logging into your account. Search for subscriptions using the search bar,click on your subscription and copy the id.
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resource group: the name of the resource group you created in the setup steps compute target name: the name of the compute target you created in the setup steps Make sure to never commit any of these details to Git / GitHub
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location: eastus, westeurope...
The purpose of this ARM Template is to deploy an Azure Machine Learning workspace with a Storage account, Container registry, Key vault and Application Insights.
But let's understand a bit better how Azure Machine Learning workspace work.
Machine learning is a data science technique that allows computers to use existing data to forecast future behaviors, outcomes, and trends. By using machine learning, computers learn without being explicitly programmed.
Forecasts or predictions from machine learning can make apps and devices smarter. For example, when you shop online, machine learning helps recommend other products you might want based on what you've bought. Or when your credit card is swiped, machine learning compares the transaction to a database of transactions and helps detect fraud. And when your robot vacuum cleaner vacuums a room, machine learning helps it decide whether the job is done.
Azure Machine Learning provides a cloud-based environment you can use to prep data, train, test, deploy, manage, and track machine learning models. Start training on your local machine and then scale out to the cloud. The service fully supports open-source technologies such as PyTorch, TensorFlow, and sci-kit-learn and can be used for any kind of machine learning, from classical ml to deep learning, supervised and unsupervised learning.
Explore and prepare data, train and test models, and deploy them using rich tools such as:
- A visual interface in which you can drag-n-drop modules to build your experiments and then deploy models
- Jupyter notebooks in which you use the SDKs to write your code, such as these sample notebooks
- Visual Studio Code extension
For more information, you can consult Azure Machine Learning Documentation
The workspace is the top-level resource for Azure Machine Learning, providing a centralized place to work with all the artifacts you create when you use Azure Machine Learning. The workspace keeps a history of all training runs, including logs, metrics, output, and a snapshot of your scripts. You use this information to determine which training run produces the best model.
Once you have a model you like, you register it with the workspace. You then use the registered model and scoring scripts to deploy to Azure Container Instances, Azure Kubernetes Service, or a field-programmable gate array (FPGA) as a REST-based HTTP endpoint. You can also deploy the model to an Azure IoT Edge device as a module.
Don't let the size of the template scares you. The structure is very intuitive and once you will see how much easier your life will be deploying resources to Azure.
These are the parameters on the template, they already have values inserted, so you don't need to worry about changing them.
Here the list of all parameters:
| Parameter | Suggested value | Description |
|---|---|---|
| workspaceName | globally unique name | The name of the machine learning workspace. |
| location | location | This template takes the location of your Resource Group. Treating appropriately the available locations for each resource. |
There are a few ways to deploy this template. You can use PowerShell, Azure CLI, Azure Portal or your favorite SDK.
If you would like to know more about it you can find more information here here. But bare in mind that you don't need to use the Visual Code app, you can stick with the always present Command Line on Windows or the Linux bash terminal.
In the terminal window type: az login
You will be redirected to the Azure Portal in your web browser where you can insert your credentials and log in.
After logging in, you will see your credentials on the terminal.
To set the right subscription, type following command:
Now you need a Resource Group for our deployment. If you haven't already created a Resource Group, you can do it now. If you are new to Azure and wonder what is a Resource Group? Bare with me! A Resource Group is a container that holds related resources for an Azure solution. The resource group includes those resources that you want to manage as a group. Simply saying: it's like a folder that contains files. Simple as that.
To create a Resource Group, you need a name and a location for your Resource Group.
For a list of locations, type: az account list-locations
The Machine Learning service isn't yet available for all locations. Check the availability of locations here.
To create the Resource group, type the command:
Super simple, right? Now that we have our Resource Group created, let's deploy the Azure Machine Learning workspace using the ./DeployUnified.ps1 script.
Script parameters:
On the portal, open your Resource Group, if you want to remove the **Azure Machine Learning workspace **, you can just click on the [Delete] Button.
Just refresh your screen and you are good to go.
Tags: Azure Machine Learning, Machine Learning, Secrets, Resource Manager, Resource Manager templates, ARM templates