>> Learn how to get started with
the Azure Machine Learning service
and how the Azure Machine Learning
and extension for Visual Studio code works
in this episode of Azure Tips and Tricks.
Here we are in Visual Studio Code.
We are going to install
the Azure Machine Learning extension that lets
us work with the Azure Machine Learning Service.
Let's go to extensions and now search for Azure Machine Learning.
There it is. Let's click "Install" and there it goes.
This can take awhile,
and it is done.
Now, I need to reload Visual Studio Code
before we can use the extension and that's it.
The extension also installed the Azure extension,
and also the Python extension for
Visual Studio Code which you can see here.
By the way, in order to use
Azure Machine Learning with Visual Studio Code,
you need to install Python on
your local machine which I've already done.
Now, let's go to the Azure extension.
I've already signed in,
but you might be prompted to do so.
Once we have created an Azure Machine Learning Service Workspace,
we can manage it from here.
Let's create one.
First, I'll type in a name and now I select my "Subscription."
Finally, my existing resource group and that's it.
It is now creating a workspace and I'll skip to when it's done.
Here we are in the Azure Portal.
I'm searching for machine learning
and select the "Machine Learning
Service Workspaces" and there they are.
There is only one, the one that we've just created.
This is the workspace and it is empty at the moment.
Let's get started by using Azure Notebooks.
This immediately prompts me to clone the
Getting Started Notebooks which I'll do,
and there we go.
Now, I'll open the first notebook file and here it is.
This is an Azure Notebook which is
the Azure version of a Jupiter notebook.
This contains scripts that can be executed and
serves as a collaboration environment for data scientists.
I'll go ahead and click "Cell" "Run All"
to execute all of the code in all of the cells.
The notebook contains code that creates
a machine learning experiment in
the Azure Machine Learning Workspace.
It does that with Python scripts
and this is the actual experiment.
This experiment attempts to estimate the value of pi.
Everything that you see here,
you can also do from Visual Studio Code and that's it.
Let's go back to Visual Studio Code.
Here, we can now refresh and then look at the experiment.
This is what the notebook created.
Let's take a look at it.
It ran a bunch of times and came up with
3.1364 for the estimate of pi. Let's go back.
From here, go to the next notebook.
This notebook deploys the experiment as a model to a web service.
Again, it does it with all Python commands,
which you can also do from Visual Studio Code.
Let's again go to Cell and Run All.
Before it is finished,
I'll delete the last Cell
which deletes the web service to clean it up.
All right. That's it.
Back to Visual Studio Code.
Let's take a look at deployments and here is the web service.
Let's take a look at it. Here it is.
This is the URL that we can use to call the service.
Azure Machine Learning Service is really powerful and you can use it
from Visual Studio Code
with the Azure Machine Learning extension.
Go and check it out.