>> You're not going to want to miss
this episode of the AI show where we
talk about the new Azure Machine Learning web experience.
It's new, it's wonderful,
you're going to love it. Make sure you tune in.
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>> Hello and welcome to this episode of
the AI show where we're going to learn
about the new Azure Machine Learning web experience.
Stephanie, tell us who you are and what you do.
>> I'm Stephanie Lindsay,
I'm a Program Manager and I work in Azure Machine Learning.
>> Fantastic. So tell us about
the new web experience, who is it for?
>> Yeah. We're building a new web experience
for a data scientists and data
engineers to complete their
machine learning tasks into end and a productive way.
So anything from prepping data,
all the way to training,
evaluate, and deploying their models.
>> That's amazing. So I feel like with
anything that's a new web experience,
I feel like we need to see it.
Maybe can you give us a little bit of a tour?
>> I would love to you.
>> All right, let's do that.
>> Great. If you come to my screen, we're on ml.azure.com.
So here I've already signed in with my Azure account.
I'm going to select my subscription.
It turns out my IT admin has already created a workspace for me.
So I'm just going to select
my existing subscription and
my existing workspace that's already set up and created for me.
So I can just go ahead and get started.
>> So it's interesting that you said IT admin,
which seems like it's a totally
different person that's doing this.
>> That's right. That's right. Yeah. We really wanted to
understand and learn from our customers
at different experiences for different personas.
So our IT pro may be more focused
on setting up the infrastructure, the security,
assigning roles permissions managing quota,
whereas our data scientists and
data engineers they really want to be focused more on
those data science scenarios and
have their own place to work for that.
>> Fantastic. All right. Well let's see what we got.
>> Okay. So I signed in,
here you see a welcome screen because it's
the first time that I've actually assigned in.
>> That's cool.
>> We really wanted to provide some guidance to our customers,
especially if they are new to machine learning.
So you really want to guide them through the end-to-end experience
from prepping the data all the way to deploying the models.
So if I take a quick tour,
you can see we have
three different authoring experiences and it's
really catering to different skill levels.
So I might be someone who is new to machine learning and I really
want to leverage the power of
the platform to select the best algorithm for me,
or I might want to use
a drag-and-drop interface because I don't know how to code.
>> Sure.
>> So I just want to a simple interface to use,
or I might prefer notebooks which
is more of a code-first experience.
Now, regardless of the authoring flow that I choose,
all my assets are created and placed here in one place.
So they're easily discoverable and I can
also share them with other data scientists in my workspace.
>> That's cool. Because you don't have to write
down where did you put the data now it's everywhere.
>> Exactly. It's really easy to find.
Then I could also go and manage some of
my resources and my workspace.
>> That's awesome.
>> If I have multiple workspaces,
I could just go and switch between the two pretty easily.
>> So is this designed with the idea that
there's multiple data scientists working together?
That's right. That's right.
We really wanted to make everything
easily discoverable because we learned from
our customers that data scientists really
want a common place to share,
review, and reuse the work from others.
>> Got it.
>> Yep.
>> All right. What we've got going on here?
>> All right. Let me show you the notebooks experience.
So I'm going to just show you
the file gallery here or File Explorer.
So this is the Azure Machine Learning gallery.
So we want to make it easier
for our data scientists come in and get started.
So we have a lot of samples that you could choose from,
some how-to tutorials, as well as some quick starting guides.
>> Sure.
>> I could also just easily clone
this whole folder to my user files.
>> I see.
>> So if I click here,
I can just clone and let me collapse this real quick.
So here's my user files.
>> I see.
>> So you can see I have a bunch of files that already
have and then you see a bunch of other users in my workspace,
so I can go and see what they're working on.
>> That's cool.
>> Yeah. So let me go and select a sample real quick.
I have that tutorial here that's already ready for me.
I just click on it here,
I'm going to select my notebook virtual machine.
So this is really just my machine that has
everything set up for me and then I
can go and run my notebook on it.
>> She is running in the Cloud?
>> Definitely. For sure.
Then if I wanted to,
I can just create a new one if I didn't already have one running.
So for this one, this is just a sample template and obviously,
I could go replace it and use my own data and train my own models.
>> That's cool.
>> So this is just a template,
let me just show you how to run a cell.
So I can just click on here
and then I can just quickly run a cell.
You can see in this particular cell,
it's showing me the latest version
of the Azure Machine Learning SDK.
>> So it's like a standard notebook experience,
but built into Azure Machine?
>> Yeah. It's all integrated.
Now if you prefer,
you can actually go click out and go to Jupiter directly.
>> I see and that would run Jupiter on that VM?
>> Yes.
>> Oh, that's cool.
>> Yeah. Now, I may
be someone who's not as comfortable with notebooks,
so I might want to use a different authoring flow.
So I can switch over here and go to Auto ML.
So I'm sorry, switch over to
Auto ML and I want to quickly create an experiment.
So I can do that,
I can either create a new dataset,
or I can select an already existing dataset.
Here, I'm going to go ahead and use
my automobile price data because
I want to train a model that can predict
the price of a car based on a certain set of features.
>> This is cool because you can have any number of
datasets that you've all been working on together.
>> Yeah. That's right. So I'm going to click "Next".
Here, I'll just make up an experiment name.
I'm going to select the column that I want to target and
here I want to really target the price.
>> Got it.
>> Then I can select a compute or
to create an existing or a new compute.
>> This is cool because usually you have to
scratch like the password somewhere,
and now this is all just in the same place.
>> Exactly.
>> Cool.
>> So I'll just click "Next".
Here I want to choose "Regression".
I have some advanced configuration settings
that I want to if I want to go deeper,
click "Done" and now it's creating a new run for me.
>> Got it. So you really didn't have to do very
much other than pick the data and
say do the automated machine learning.
>> Exactly. That's right.
Here it is you can see this status,
it's actually starting right now.
But let's flip over and we can choose I already ran an experiment.
Here, I see I've run a couple of
different experiments and I can see my dashboard.
So here's some have completed,
some are running, here's the one I just ran.
>> That's cool.
>> So we can dive a little bit deeper,
I'll just click on this one real quick.
So for here it ran over a 100 different iterations
and it found the best model for me and so quickly I can see that.
If I wanted to look at all the other trials,
I could and I could explore those little bit more.
But once I I'm ready and I'm comfortable with the best model,
then I can just click "Deploy best model" and I'll just give
it a name, Deploy.
>> That's cool.
>> Yeah. It looks like I already have one that's called that.
So I'll just change that.
So while that's going,
that'll take a couple of minutes.
But then once it's done,
it's actually here on my endpoint.
So coming back to earlier,
I can see all of my different datasets,
all the different experiments that I run,
so I can see experiments I just kicked off,
the models that I just created,
as well as the end points that I created after I deploy my model.
>> This is pretty cool. It's like a single place where
everything you would ever need to do
machine learning lives in the same place.
>> That's right.
>> I think what's even
more interesting to me is that you were thinking
about the different persona's and
you organized things in that way to.
>> That's right.
>> Which is pretty cool.
>> Well, where can people go to find out more about this?
>> They can just visit in ml.azure.com.
>> Fantastic.
>> Yeah, get FML started today.
>> Fantastic.
Well, thank you so much for spending sometime with us.
>> Yeah, thank you.
>> Thank you so much for watching and learning all about
the new Azure Machine Learning web experience.
I hope you go to it. Thanks for
watching. We'll see you next time. Take care.
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