>> As a developer I'm really glad that I
can do everything inside the Visual Studio Code.
But I do think that it's important that I
learn some of the tools that data scientists
and machine learning scientists use mainly so that I
can work with them more effectively
and also so that I can get their help.
In fact, I need some a Francesca's help.
So let's get all of our code inside of
Azure ML Studio so that she can jump in and help us out.
Don't forget you can get to the studio by going to ml.azure.com.
We're going to head back to the machine
learning resource in Azure,
and you'll notice this try
the new Azure Machine Learning Studio notification
in the Overview tab.
I'm going to click "Launch now".
If for some reason that's not there on yours,
you can always head over to ml.azure.com
and you'll be able to find the machine learning
resource that you created over in Azure.
The first thing I want to show you is the notebooks feature.
Francesca gave us a brief overview
of the Azure Machine Learning Studio,
and now I want to show you how to use it practically.
I'm going to create a new folder
so that I can mirror what I was doing on my local environment.
We'll call this devintrods,
I'm pretty creative with my naming if you can't tell.
Inside of this folder,
I'm going to create a new Jupyter Notebook file.
We'll call this bike-share-in-the-Cloud,
it's a long name but that's okay.
We want it to be a Python notebook file.
Notice that we have this requirement of a compute and it
says no computes found with the dropdown not being available.
We do need to create a new compute
for this Jupyter Notebook to be hosted in.
We did create our compute for our training data,
but if you remember that was
under training clusters, not compute instances.
It's specifically reserved for training our data.
let's head back to our notebooks view and create
a new compute for hosting this Jupyter Notebook.
I'll call this one jupyter-compute,
and just for consistency let's keep it the same
VM size as the one that we used for our training compute.
We can see the status for that compute being created here,
and we just have to wait until it's finished.
Now that our compute is running,
we can open up our Jupyter Notebook file.
It's already selected so I just have to come up to
this menu click "Edit" and choose "Edit in Jupyter".
This will open a new tab with a blank Jupyter Notebook.
Now we did initiate a lot from our local environment,
so we do need to bring some of
those local variables back
into our Jupyter Notebook here on the Cloud.
Now nothing in this Jupyter Notebook should be
a surprise to you but I'll briefly orient you.
Up here at the top we're going to re-import any libraries,
packages, or classes that we might need,
recreate our local variables,
connect up to our workspace,
experiment, compute cluster, and dataset.
This is all grabbing things from
the actual workspace using
our subscription ID, resource group name,
and resource name, our actual experiment name,
cluster name, and the default datastore.
Notice that none of these get recreated.
If they already exist,
Azure ML will just pull them right into the notebook for us.
Next, we're going to connect up to the run that just completed.
Notice that I had to have a run ID here.
I could have gotten this from two different places.
One is VS Code,
it's just right here at the top.
If you've already closed VS Code
or for some reason you've deleted this file,
you can always grab it from the Machine Learning Studio.
Head over to experiments,
scroll down to the runs and you'll find the run ID right here.
Finally, we do need to recreate our test data.
Notice that in VS Code we only ever ran our training data,
so we do need to have access to
our test data so that we can run that.
Let's go ahead and run this cell.
Awesome. It printed the first five lines of
our testing data which
means everything before it must have worked.
Now the next step is to make sure that
Francesca can get in here and write some of her own code.
For this, I'm going to head back to the Azure portal.
I'm going to go into the "Access
control" for this machine learning resource,
and I'm going to add a role assignment.
I'm going to add Francesca as a contributor to my project.
I'm not going to change any of the access and I'm going to
search for her here in the search bar. There she is.
I'll save this, and
now she's been added as a contributor to this resource.
Now when she heads over to ml.azure.com,
she'll be able to see my notebooks,
my experiments, and everything.
So hopefully she can help us identify
our best run and help us view the featurization summary.