>> You're not going to want to miss this episode of
the AI Show where we learn all
about how to use R in Azure Machine Learning.
It's pretty cool, and make sure you tune in.
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>> Hello, and welcome to this episode of the AI Show.
We have some exciting announcements.
Cool things are coming out for
Azure Machine Learning since Ignite.
I have a special guest.
Tell us who you are and what you do, my friend?
>> Hi, I'm [inaudible].
I'm a Program Manager in Azure Machine Learning,
and today I'd like to talk about
the R support coming in for
Azure Machine Learning in public preview.
>> Fantastic. So tell us a little bit
about our R support. What does that mean?
>> Yeah. So a couple key capabilities
are coming in for public preview in the initial offering.
First will be an R SDK.
They'll have end-to-end APIs
for doing training to deployment for all the core scenarios.
We're going to have support for submitting
remote R jobs on Azure Machine Learning compute,
ability to do hyperparameter
tuning on our hyperparameter tuning service,
hyperdrive and of course,
deploying our models as
web services to Azure container instance
in Azure Kubernetes cluster.
>> So basically if you are an R enthusiast,
it does Machine Learning with R. Now you have the full suite of
capabilities that you had with
Python or is there a little bit of a mismatch?
>> Yeah.
>> Tell us about that first.
>> We're starting off with core subset of features.
So you'll get access to things like creating
managing workspaces, compute, data-stores, models,
web services, and then additional capabilities will be
coming in after we do the first initial public preview.
>> Fantastic. So I'm used to writing these things in Python.
I would just basically new up a workspace,
pass in the thing, same thing available in R.
>> Exactly. You can do all of that in R.
>> That's awesome. So tell us a bit about the AML
compute instance or sometimes called Notebook VMs.
Do we have any support for R there?
>> Yeah, definitely. So Azure Machine Learning compute
instance previewed as Notebook VM initially when it first
came out earlier this year has been super
popular especially with Python developers because
they're able to easily get an authoring experience with
all of their pre-configured Machine Learning packages
installed on these VMs,
and there'll be able to do
one-click access for Jupyter and Jupyter lab.
So we really wanted to have a similar experience for
our users in a secure enterprise ready environment.
So we have a couple of integrations with the compute instance.
There's R kernel enable in Jupyter.
So if you want to work with Jupyter Notebooks, you can do that.
The R SDK will be installed there
on the compute instance, and also excitingly,
we'll have RStudio Server,
the open-source addition pre-configured
on all of the compute instances.
So you'll be able to also get access,
one click to the browser-based ID for RStudio.
So if you're already heavily using
RStudio on your desktop or whatever your existing workflow is,
you could do that today and get started with R SDK immediately.
>> So if you have an Azure Machine Learning compute instance,
basically there's a link there that will open up RStudio?
>> Yeah, the browser based RStudio experience.
>> Well, I feel you need to show me
that because that sounds awesome.
>> Okay, sure. Yeah, so over here,
I'm already in, you can see this is
running on my compute instance.
This is associated with
the compute instance I created from my workspace.
So over here I just have a simple script that does training
on remote Azure Machine Learning
compute for the Iris flower dataset.
You can see we have all of
the same things like getting workspaces,
accessing datastores that you could do in Python but now in R.
>> That's cool because I'm looking at it,
and basically you have things like Git workspace.
You have things like Git default datastore,
and you have the other thing.
Because I'm looking at this and I'm
not as familiar with R as I would like to be.
But as I'm looking at this,
I'm seeing basically the same structure
of things that you would see if you were doing in Python.
>> Yeah, exactly.
>> That's cool.
>> So if you're already familiar with the Python APIs,
it's a pretty easy transition.
If you're not, it's easy to get started.
We have all the samples and notebooks online.
So yeah, we can see over here,
we're importing our library,
so you can install the Azure Machine Learning SDK.
If you're doing this locally from CRAN,
it's just going to be published there,
all of the official releases,
or the other exciting thing is our R SDK is open source.
So it's going to be over here at this GitHub repository.
So all of the latest updates from
our team and our developers will be
made to that GitHub repository.
So if you want to access
the latest developments and you
don't want to wait for the next release to CRAN,
you can simply install or update from
the GitHub repo and of course,
contributions are welcome from the community.
>> That's really cool. So basically
the whole SDK is open source for R folks.
>> Definitely.
>> Because now I'm thinking it.
Basically, you're just making API calls
to the services and you're just wrapping it with R.
>> Yeah, so it's pretty easy to follow.
If you look at the source code,
it's open source under the MIT License for Microsoft.
So we just want to make sure that from the get-go,
anyone that's interested from the community will get
full transparency into what we're doing.
>> Fantastic. So if people want to get started, basically you can,
I don't know what they call in R. I know CRAN is
the repository pip for Python.
>> Exactly.
>> Basically just reference
the library and it will be pulled down.
>> Yeah, so just install. There's a simple API call
for that and you'll be able to install it.
>> Fantastic. Well, this is amazing.
Anything else you'd like talking about?
>> I think that's it.
Another last thing.
So the existing functionality we
have in Azure Machine Learning Studio
for a UI first experience with running your R workloads,
that's still there and we'll be making
sure from public prev and onwards,
we're just going to make that experience even better.
>> Fantastic.
>> For all our R users.
>> So I'm glad you tied a bow around it.
Now we have full R, the R experience,
and using Azure Machine Learning.
But then we also have it in
the designer as well where you can put R.
>> Exactly.
>> Like you use to in Studio.
>> Exactly. Yeah, so we want to still maintain UI first or
a Kudrin reverse approach depending on
our users and customers preferences.
>> Fantastic. Well, this is amazing.
>> Yeah.
>> Thanks so much for coming.
>> Yeah, thanks so much for having me today.
>> Thanks so much for watching and learning all about how
we support R in Azure Machine Learning.
It's pretty exciting. Make sure you check it out.
I'll put some links below to the GitHub repo so you can do it.
Again, thanks for watching. We'll see you next time. Take care.
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