>> You're not going to miss this episode of
the AI show where we introduce a new skew-
>> Skew.
>> -into Azure Machine Learning.
The enterprise here, you want to learn all about it in
the next episode of the AI show, make sure you tune in.
[MUSIC].
>> Hello and welcome to this episode of
the AI show where we have a special guest to
talk about a new and exciting skew in Azure machine learning.
Tell us who you are and what you do my friend.
>> I'm Shenee Winon and I'm
a program manager on the Azure Machine Learning platform team.
>> Let's start off first with what prompted
us to introduce a new skew into Azure machine learning.
>> As you know our product has grown
tremendously since we made it publicly available.
When we did that, we saw
tremendous growth and we said we still want
to support our open source developers and our data scientists.
But we also want to add an enterprise offering where persons from
all different machine learning backgrounds
could actually achieve machine learning solutions.
So we've extended our offerings to include the enterprise.
>> Fantastic. Do we have
an enterprise skew and
a something else skew, what are the two skews?
>> Yes, yes. Let me just bring up this slide here.
>> Got it.
>> As you can see, we have a basic skew and an enterprise skew.
This basic skew is really
a one stop shop for our open source developers.
In this skew, people who are really familiar and
comfortable with the code first experience will come here.
If you want to manage your own machine learning life-cycle,
the basic skew is for you.
You can access this as
it currently exists today through our Azure portal,
our SDK or CLI.
>> So it's basically what we already had,
every data scientist that already has
code that's used to putting stuff
into containers or running stuff and
just the whole life-cycle of machinery.
But it's a code first professional experience,
the basic skew is for you.
>> Exactly.
>> Got it.
>> Exactly. Then, on the other hand,
we have the enterprise scheme and then this skew
we have some added capabilities,
for instance, we have enhanced security,
collaboration, manageability, and governance.
For example, in this skew enterprises can control their resources,
they could do capacity sharing and quota management.
>> Got it.
>> Also in this skew, there's machine learning for all.
We have some really cool new UI capabilities
like Self-serving UI and Drag and drop UI.
Then also we've improved
our MLOps capability to
streamline the machine learning life-cycle.
>> That's amazing. Basically, we have what we had before but
now we're adding stuff for people that are
maybe not pure data scientists.
>> Exactly.
>> We're giving it more of a look and feel
for someone who's like a citizen data scientist,
but we're also adding effectively
enterprise level things for sharing and etc.
>> Exactly, yes.
>> What kind of things are in
the enterprise skew? Why don't we talk about that?
>> Sure. As I said,
we still have support for our back VNets.
Users will be able to use the manager resources more.
But moving on to what I think is really interesting is the new UI.
>> Cool.
>> Let me dive into the new studio experience.
>> Let's take a look at it.
>> As you can see here,
this is where we users will come and interact
with our product through our studio.
Then we have the self-serving UI
called Automated Machine Learning.
>> Got it.
>> If we go here, users have the ability to select
the different type of models that they want to use for training.
For example, we have classification,
regression and time series forecasting.
Then you can actually train your models through the UI.
You could see model comparisons.
After this, you could use some of
our high-end technology to
perform modal interoperability and explainability.
>> I see. This is interesting because as I'm looking at this UI,
there's no notion of I had to do something with Scikit-learn.
I basically just said go do
a thing with my data and then let's see what happens.
>> Yeah, exactly, yeah. So users will come here,
it's machine learning for all basically.
>> Fantastic. What else is available?
>> We also have the designers.
The designer is the visualization component for pipelines.
Azure machine learning pipelines is really
just a workflow where users can
stitch together various phases of machine learning.
You can stitch together like
data preparation, model training, deployments.
>> Got it.
>> So pipelines really can consist of
one step as you can see here in
this slide or multiple steps, as you can see here.
This really gives users the opportunity to
come here, create pipelines,
view them, visualize it,
publish their pipelines and endpoints,
all from our UI.
>> Again, I felt like
automated machine learning was something
that you just said here go do a thing.
Here is I'm going to do a thing but I'm not going to get
super involved with the nitty gritty, I just drag and drop it.
>> Yeah. So you have total control here.
Once again, this is create a few,
I'm going to also edit these modules.
So you have the experience of using it
from the UI and also through code. Yeah.
>> That's awesome. Let's just say someone already
has an existing Azure Machine Learning workspace,
how do they access these new capabilities?
>> Yes. As of right now,
all our users will have default workspace which is basic.
You could simply upgrade to
the enterprise workspace and to do that currently,
you could do so through the Azure portal.
So you could either create
a new enterprise workspace or
upgrade your existing workspace enterprise.
>> Fantastic. Let's talk about cost.
Obviously, there's going to be a difference in
costs. Let's talk about that a little bit.
>> Yes. So there's a difference in costs.
Actually, the basic skew is we're
dropping the surcharge on some of our compute.
So for AKS, we're dropping it.
But for the enterprise skew,
there's an additional cost.
There will be a surcharge on our compute targets and
I suggest visiting our pricing page
to learn more about the change in costs.
>> Fantastic. It's pretty exciting.
We have a basic skew for if you've already been doing stuff,
it's pretty exciting, You can still do that.
It's all code first,
you're a professional, you can do your thing
yourself as a scientist.
We're adding an enterprise skew that helps
with people sharing inside of a workspace.
It also helps with some visual tools to
do machine learning if you don't want to code it up yourself,
you can totally just have us pick it for you
or you can drag and drop designers style away.
>> Exactly.
>> Where can people go to find out more to get started?
>> Yes, I would suggest visiting our technical documentation.
You would see lots of cool new features
that we're introducing across different feature areas.
Also, I'd suggest visiting our new studio.
You'd find tons of useful links and videos
and tools to learn more about the product. Yeah.
>> Fantastic. Well, this has been amazing.
It turns out you can actually create
your own workspace for free if you want,
as you can see behind us.
So there's no reason why you shouldn't be able to get started now.
>> Exactly. So check out the two new skews,
the basic and enterprise skew and hope you enjoy it.
>> Thank you so much Shenee. Thank you so much for watching.
We're learning all about the new enterprise skew
in Azure Machine Learning.
Thank you so much for watching and
we'll see you next time take care.
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