Hello, internet.
Welcome back to the Data Mining
with Azure Machine Learning
Studio by Data Science Dojo.
Today's video, we're going
to go ahead and create
on Azure subscription.
We're going to create
an Azure ML workspace
within that subscription.
We're going go in and explore
the features within that Azure
ML workspace.
And if you already have
an Azure ML workspace,
go ahead and skip this video
and go to the next video, where
I jump straight into building
experiments, importing,
and exporting data.
OK, so the first thing
we're going to need to do
is we're going to need to get an
Azure free trial subscription.
The first thing you can
do is you can either
type in this link.
Go ahead and pause
this video and type
in this link to your browser.
Or you can just go to a search
engine and then type in Azure
free trial, and it should be
the first link that shows up,
so
azure.microsoft.com/Azure/free
trial.
So go ahead and click on that,
and then it's going to go ahead
and say Start free trial.
You will be prompted to log
into a Microsoft-type account.
So that is like an email
that is like @live, @outlook.
I believe Gmail is supported,
so you can try that as well.
So if this is the first
time you've ever signed up
for Azure, notice you'll be
brought to this page where
you'll have one month
that is a free trial,
and you will get $200 of
credit in that first month.
So whatever one hits first,
whether it be 30 days or $200.
All right, so when signing
up, you'll need two things.
You'll need a phone number and
a working phone with that phone
number.
They're going to text
you a verification code.
The next thing you'll
need is a credit card.
OK?
So they need this to
verify your identity
to make sure that you're not
a bot making subscriptions
to then create
more subscriptions
to make more bots.
Another thing is they're
trying to make sure
that you don't go
from month to month
with a different email getting
free Azure stuff for free.
All right, so they're not
going to charge this credit
card at all.
It's just going to be used
to verify your identity.
Now, those of you, I think, who
have banks in China and India
might get charged some kind
of $1 verification fee.
That is dependent on
your bank, I believe.
All right, so once you have
your Azure ML subscription,
to log into that
subscription, you
would go to Portal.Azure.com.
I'm going to go ahead and
paste that into my browser.
Now, you can go ahead and just
go to a search engine and also
just type in Azure as well.
It would, I think,
Azure.microsoft.com
is what you'll be sent to.
You can just click on
the Portal at the top.
I'm just going to go ahead
and log in right here.
This should be a screen that
you should see when you log
in to your Azure subscription.
So you'll notice, this
is our main dashboard.
This displays a bunch of tiles
that is very akin to Windows.
So what we're going to go
ahead and do now is notice
that these are all the
app services that we can
go ahead and spin up
in the Azure Service,
but we're here for a
very specific service,
and that is Azure
Machine Learning Studio.
So to make a new workspace,
or to make a new anything
in Azure, go ahead and
click this New button,
on the top left hand corner
of your subscription page.
So go ahead click New.
You're going to type in
and search for a service
called Machine Learning,
and once that is there,
you're going to
look for something
called Machine Learning
Workspace by Microsoft.
OK?
So click on that,
and then you'll
get a brief description about
what Azure machine learning is.
Go ahead and then click Create.
OK, so you'll be then prompted,
and so this thing over here
that just popped up is
what's called a Blade.
So in this blade,
you'll be prompted
to enter in a bunch
of information
about this workspace that
we're about to create.
All right, you get to
name the workspace.
So I'm going to go ahead
and name it, I don't know,
Phuc Workspace.
Yeah.
I'm going to go ahead and--
so if you have multiple
subscriptions, which I do,
you'll see this drop
down box over here.
If you only have
one subscription,
you probably won't see
this subscription here,
but it lets you
basically pin this Azure
asset to that subscription
to be built to.
All right, so the next thing is
we need to basically pin this
to a resource group.
So you can use an
existing resource group,
or you can create
a brand new one.
So what a resource group
is it's a logical container
that binds cloud assets together
for billing and automation
purposes.
So the idea is you would
pin a bunch of Azure assets
that are doing the same
task, or the same job,
or for the same project,
to the same thing.
Think of it like
a folder, but for
your online cloud-based assets.
So that is, if you delete
this resource group,
it deletes everything
in the resource group.
It's all built together, and
it's also automated together.
You can spin it
all up once, if you
know how to do PowerShell
scripting or things like that.
For new users, you tend
to not care about what
this resource group thing is.
Just go ahead and create one.
So I'm going to call--
I normally like to
name my resource group
the same thing as the asset
that's contained in it.
So I will call this
workspace Resource Group.
OK, and then you'll be
prompted to, which data center
do you wish to use?
So the location
of the data center
matters if you need to
take in a lot of data
or if you need the
output a lot of data.
So normally, you are
not really charged
much for bringing in
data to the cloud,
but you're charged a lot
for actually taking data out
of the cloud.
So I would recommend
that wherever
you want to consume the
final output of the data
is where you should go ahead
and set the data center to be.
So because I want to do
everything in the US,
I'm going to go ahead
and select Central US,
and then it's going to ask us
to create a new storage account.
So the storage account as a
separate service within Azure.
It's called Azure Blob
Storage, and what this is
is basically cloud storage.
Remember, you're getting
charged about $0.02 per gigabyte
per month to store
something in here.
So this is where all your data
is going to be backed up to,
and this is where all the Azure
ML experiments can be saved.
Now, if you delete this
storage container later,
it's going to go ahead
and, your workspace
won't be deleted, it
will just error lock,
because it no longer has the
data it was referring to.
And the name of
this workspace will
have to be a globally unique
name, because this will become
a URL for your cloud storage.
So think of it like a domain
name, like PizzaHut.com
or something like that.
So this check mark
over here will tell you
that it is free and
clear to be used.
It all has to be
lowercase, and all
has to be in letters, no
symbols, no numbers, nothing,
all lowercase and just text.
So it's gone ahead and
named it for me, so
Phuc Workspace storage.
OK, I'm fine with that.
And then, the for
the pricing tier,
I think you can just
leave that standard.
I think it only has
standard right now.
I think they're beta testing
some other tiers, right now.
And then we're going to go ahead
and for the web service plan,
unless you really know
what you're doing,
don't set anything here.
And basically what this
is is it will set--
when you deploy web
services, you're
picking what kind
of tiers of service
you want for that web service.
How robust do you want
that service to be?
How many people and transactions
do you want it to support?
For the most part,
the free one is fine,
where we have 1,000
transactions is fine.
OK?
So we'll create a brand new
web service plan for that,
and we'll select that as a
tier, which is no pricing.
We'll select the standard
tier, which charges us nothing,
but we only get 1,000 API calls.
Which I think is more
than fine, especially
if we're just prototyping.
OK, so once we filled
everything out here,
I think we're good
to go, we will
want to pin this asset
to our dashboard.
So remember those tiles
we saw at the beginning?
That's where we want
our asset to be,
so we can always
refer to it later.
So go ahead and click
the Create button now.
So this will take about
two minutes to create.
Go ahead and do something
else for those two minutes.
You will see a tile that has now
appeared, because we selected
that button.
It says Pinned to the
Dashboard, and this
is going to spin for
the next two minutes.
All right, it looks like
it's finished creating,
and it automatically
brought me into the asset.
But if you don't know
how to get back here,
if you're on the
dashboard, you can just
click on the tile
that was pinned.
So click on the tile and
you'll get back to this page.
So this page lets you basically
manage the Azure asset that
is the Azure ML workspace.
And now to get to
the workspace itself,
you will click this button
under Additional Links that says
Launch Machine Learning Studio.
Now, what I actually
prefer is this actually
goes through a separate website.
So you can actually
go to Azure ML
by just going to
Studio.AzureML.net.
That does the same thing.
So if you take this URL and
paste it into your browser,
it'll take you to the same
place as clicking this button.
So I actually prefer this
URL, because it cuts out
the middle man.
Because the idea is, all right,
I have to log into Azure,
and then once I'm in Azure,
I have to find my asset.
I have to click on my asset.
Once I click on my asset, I have
to click on this Launch Machine
Learning Studio.
Or, if I just want
to use Azure ML,
I will just go
directly to this URL,
and I'll cut out
Azure altogether.
I'll go directly into
Azure ML Learning Studio.
That's just a tip
that I'll give to you.
All right, and it's going
to ask you to sign in again.
It will share your
Azure subscription.
So that's fine.
So go ahead and log into
your Azure subscription,
but this time do so by logging
into your Azure subscription.
So notice, I'm inside
of Phuc workspace.
So that is the name
of my workspace.
So notice that you can have
lots of different workspaces,
and notice that you can
select different regions
and things like that.
So there's nothing stopping you
from having lots of workspaces.
So you can also change
workspaces up here.
So I notice I don't have
anything in West Central US,
but I think if I go
to South Central US,
I have four other workstations
I can select from.
So notice, these are all
self-contained workstations
that are either been,
A, shared with me,
or I'm hosting them on a
separate account somewhere.
So let's go back
to our current one.
So that's how you
switch workspaces.
Now, the reason for this is
if you go to, for example,
the Setting button
over here, you
can invite users
to your workspace.
So that is, if you have a team,
if you're working on a project,
you make a new workspace and
invite all your team members
to that workspace.
And that will be a
self-contained workspace.
So this is the closest thing
that we data scientists
have to a Google doc right now.
This is probably one of
the coolest collaboration
tools we've had in a while.
So you can always do this.
You can invite more
users, for example,
and then I can invite my buddy
Eric at datasciencedojo.com,
for example, and I can go
ahead and make him a user.
So now, Eric can
go ahead and log in
to see the same experiment,
data models, everything
that I would see
in this together.
And then, notice that if I want
to switch teams or projects,
I would switch the workspace.
Remember, you're charged
$9.99 per workspace,
so keep that in mind.
So the data sets
that you will bring
into Azure will be saved here.
They are basically objects.
Any models that you
train will be in here.
There's also a new feature,
which is Notebooks.
So a brand new
way of programming
that's taking the programming
world by storm is Notebooks.
The idea of a
self-contained environment
that can be deployed to the web,
where you just code on the web,
and then you can use almost
any language that you
would want to.
And you can share
those notebooks
or just expose them as web
services, which is really cool.
So right now it supports
Python and R Notebooks.
So those are the primary
programming languages
right now in the open source
world for data scientists.
If you have any deployed web
services, they would be here,
but your bread and butter
will be this guy right here,
the Experiments tab.
So an experiment is what they
call a file, for data science,
instead of Azure ML.
So just like a spreadsheet.
A spreadsheet file in Excel
is called a spreadsheet.
Right?
And a Word document
called a document.
These are called
experiments, and then you
can pin multiple assets together
into what's called Projects.
And notice, I can Create a
Project, name the project,
and then I can pin various
experiments, web services,
and data assets to this project.
So notice I can have
multiple projects going
on at the same time
for the same team,
and everything will be good.
And how I got here was,
see this thing in the top
left hand corner here,
you can click here
and click on the Studio.
So Azure ML actually has three
other pages associated with it.
For the most part,
you almost always
want to be in this
Studio Mode, right here.
OK?
And I think we're out
a time for this video.
Go ahead in watch
the next video,
where I will go
ahead and show you
how to create your
first experiment,
import data, export data.
And if you want to see more
videos like this in the future,
go ahead and like and subscribe,
and I will look forward
to seeing you at our boot camp.
See you next time.
