>> All right. Why am I starting with all, right?
It sounds weird. I mean but it is all right.
It's all right.
Welcome to this episode of the AI Show
where we're going to be looking at
the entire AI process using animal services,
here with Parashar Sha, did I say it right?
>> Fantastic. How are you doing, my friend?
>> I'm doing great. How are you?
>> Good. Why don't you tell us
a little overview what were you talking about?
>> Okay, sure. So today,
I'm going to talk about
the Azure Machine Learning Services,
which are currently in public preview,
and we're going to walk to
an entire experiment where we are going to predict
the income of a particular person
where you provide inputs based
on the US government census data.
We are going to use a
classical machine learning approach,
but the idea is to showcase that how far
for that Azure Machine Learning Services
are and what you do with it.
>> This has a soup to nuts.
Has a soup to nuts from beginning to end, all of our MLs,
our Azure ML services,
how you use them, why you would
use them, and what we can provide.
>> Perfect.
>> Fantastic. All right. Let's see what we got.
>> Okay. So let's start with
the Microsoft AI platform basically.
So the Microsoft the platform,
if I can say there are three particular components to it.
One is the AI services,
now there is the AI infrastructure,
and the third part is the AI tools.
Now, we have tried to see
different needs from different customers.
Everything can be satisfied.
So the AI services allow you to use
our pass based services be it in terms of
the cognitive services the API slight cost of vision,
speech, and things like that,
or if you are to create bar stand,
then you can use the back framework and things like that.
And the third part is the custom machine learning.
So a lot of enterprises, our customers,
would want to create
their own machine model
and then deployed in cloud on there.
So that's what we
call is Azure Machine Learning Services,
and that is what I'm going to talk to about today.
>> Fantastic. And so we've got a bunch of things
that people don't want to
build their own and use them services,
and they want to do conversational
type things about framework.
If you could focus on
what we're going to be focusing on today,
what are the elements of
the AI platform that we're going to talking about today?
>> Sure. So today,
I'm going to walk you through
an actual demo where we are building an application.
And we are using a remote.
Think of it this way.
Data scientists, what they normally do is they
train on a particular machine
and they will start it with a sample data set.
They would explore the data,
and then they want to scale it out
and that's where the power of cloud comes in.
Once they are happy with
the particular machine model,
they actually wanted to deploy.
And that will be another thing. So this
will be the actual process I'll walk you through,
that you start training on your local machine,
then actually scale it out using
a data sense virtual machine which has a GPU.
And then once you're happy with the model,
then you'd deploy it in the cloud in a cluster.
>> I see, does this includes services for,
not only just like deploying and having out of the cloud,
but also for training and
for how to actually run the entire experiment.
>> That's right. And the good thing is
you'll be only based on your usage,
so the pricing would be similar to what you
would pay for spinning up a regular virtual machine.
>> Awesome. So show us how it started.
>> Perfect. So let's look at
the AI development life cycle
and where does Azure Machine Learning Services.
So anytime you want
to create a machinery based application,
you would have your data somewhere.
Now either that data is coming in
real time or it has been stored somewhere.
So once you have that data,
you want to actually start creating features out
of it because that's what
is needed for machine learning experiments.
So that is what we call a data preparation part of it.
And then once you have created those features,
you want to train
a machine learning model on top of that.
So that's the prep and clean part
and you can use different compute,
different storage options in this.
And then once you're happy with that,
you actually create the model and you use it for so.
So that is the area where
Azure machine and new services fits in.
And let's look at the capabilities which are now there.
So we already have an Azure Machine Learning studio.
As you know, it's been there from 2014.
And then we got feedback
from the customers that we want more,
"We want to use different computer options.
We want to use different frameworks."
And that's where we launched our new capabilities
in September during our Ignite event.
So now, what we allow users
is whatever IDE you are using,
whatever framework you are using, you can use that,
you can trim your model,
and then once you're ready,
you can deploy it, be it in the cloud.
You can even deploy it on Prime
or you can even deploy it on IoT Edge.
>> So there's just a lot more options
on what you want to do, we can help you with that.
>> That's right, a lot of flexibility.
So now, in the demo,
what I'm going to walk you through is
this entire process of how you would actually
use a Jupiter notebook and then deploy
training using all these like I said
and then deploy it in the cloud.
>> So let us start from the very beginning.
Let's just say first time
I'm looking at Azure ML and I want to start with this,
how do you get that service started?
>> Sure. So for the set up part,
we have any user would
need at least three things to start.
One is an experimentation account.
Now, this experimentation account is actually maintaining
all your RAM histories because
that is how normal data scientists would go after.
He would do might to put it on some to figure it out.
So we moved in that history.
So anytime you wanted to go back
in time and look at something, you can do that.
Now, that thing is also batching
different remote compute targets.
So you could use, like I said,
single vertical machine, like Data Science VM
or you could even scale it out with spark-based.
So actually, you need some spark or something.
The other part is the model management part.
So once you had the model,
you can actually deploy it,
and that is the model management part.
So you have to create these two,
if you could use the portal or you could use the CLI.
And the third thing you would need is to install an ID.
Now, the ID part is optional, whatever,
I am going to show you, because you couldn't
use your ID and still use these two.
>> Alright. Visuals to your code has tools for AI.
>> That's right. That's what we have.
So let's go to the portal right now.
And I already have a screen loaded, but essentially,
the way you would do it is just do
what it creates resource,
then select the AI plus Cognitive Services.
Once you are in there, you can see on the top you
have experimentation service online on management.
Now, this is normally a one time thing,
so it's not that a data scientist would need to do that.
So it could be like account admin
would set it up once and then,
it would be shared by multiple data service.
>> And the reason why you would have
an experimentation service is for example,
let's just say you're trying
to figure out hyperparameters for a particular model.
If you use the experimentation service right,
you'd actually be able to see the history of what you've
already tried sort of Sally, data scientist,
comes in, she won't be trying what
Bob did a year ago and it didn't work,
and it's just a way of keeping track
of the work that has happened with the modeling.
>> That's right.
>> That's fantastic. It's the way you
set it up just like any anything else on it.
>> That's right. Yeah. You just provide the name,
select the subscription, the
resource group and how many seats you have.
So the first two seats are free, but if you want more,
then you have to pay based on the contract you had.
And then when you created the experimentation service,
we give you the option to create
the modern management to have an account right away.
So you don't have to go to a separate.
>> And you get the model management is to store
the actual output of
the training, which is the actual model.
Let's just say it's the weights of a deep learning model
or the decision tree or whatever,
as you say you're using something else.
It's just the numbers that you can do inferencing with.
>> That's right.
>> Okay.
>> So that's the particle HTTP API,
which you can invoke in
an application and whatever you want.
>> I see some experimentation is for when
you're trying to figure out the best model.
Model management is for when you have
a model or maybe multiple models and
that model gets better because the data gets out date or
stay a model and you
can just use the model measure for that.
>> Yes, and track everything they come in and
requests are coming home and
the responses you are getting at least.
>> Awesome. And the third thing you mentioned was
the actual infrastructure where
you run the experimentation,
which is let's just say you don't have
a huge GPU or you want to
be able to have a lot of RAM, for example.
So that's when you would use like
a Data Science VM to run that stuff.
>> That's right. And probably,
I can show it to you right now.
You just
select the size.
I do multiple options.
In this case, you can pick up the Linux.
It comes screen starting with
all the frameworks and libraries.
And then you charge based
on the type of computer you select.
So you can associate a GPU or CPU [inaudible].
>> So this, the purpose of
the Data Science VM is because, I don't know about you,
but when I set up my environment
for doing Machine Learning
or Data Science the first time,
I'm looking at selling a ton of things.
And then like if I forget what I
install and on the target machine,
it's suing the inferencing, it's not there.
I guess what this creates
a standard environment
where everything can happen.
>> That's right. And we have tested all the
interoperably of the libraries
they're made it easy for you.
>> Fantastic. So that's the set up.
Is there anything else you need
to do to get everything going?
>> No, not for this except that, like I said,
you could use your own ID or you could install of an ID,
which comes with Azure Machine Learning operation.
>> Fantastic. So this again is
just a really quick overview
of what you do in the Data Science process.
How we can help with services,
we talked about three experimentation: Model,
Management, and DSVMs, Data Science VMs,
if you want to set those up.
Honestly, I don't know how you
couldn't use something like
an experimentation service and
have models that come out better every time,
like if there's no way to do that,
and I love the idea of actually
keeping track of all of the models that you have
and making sure that if a model goes
stale that you put another one up
there and then the services sort of catch up.
It's awesome. The set up is pretty easy.
>> Yes.
>> It's awesome. So in the next
in the next installment of this,
we're going to be talking about once
you have all that set up,
what do you actually do? Does that work?
>> Yes.
>> All right. We'll see you then.
>> Yeah.