>> You're not going to want to miss this episode of
the AI show where we show you how to upload
your own ML models and deploy
them with a single click. Make sure you tune it.
[MUSIC]
>> Hello and welcome to this episode of the AI Show,
we're going to talk a little bit about machine learning models.
Tell us who you are what you do, my friend.
>> Hi, I am Shivani Patel and I work on
Azure machine learning specifically machine-learning for DevOps.
>> Fantastic. So let's talk about
machine learning models. What are they?
>> So basically Azure machine learning model is a set of files.
So you have your pick-up file,
dependency files to be able to run your model.
It's encompassing all of that.
>> So it's basically a file or set of files
that let you run the machine learning model.
Now, in Azure machine learning service,
we have the ability to save
these things into something called a model registry,
why don't you tell us what that is for those who don't know.
>> Sure, so basically a modern registry
in simple terms it's just a model store.
It's a collection of all of those files plus
a bunch of metadata that we collect on top of it.
So you make sure that you're able to track
all the information related to your model.
>> I see. So if you're a dev,
this is like a way of versioning
your assembly or DLL files
or whatever. It's basically the same thing?
>> Exactly, for data scientists to be able
to store and version all of
these models instead of just having
them be in a bunch of files on their local machine.
>> Or e-mailing them to each other which I know you do.
You need to stop doing that.
So what's new in the model registry coming up?
>> So right now in the model registry,
you basically have your model,
your model version, which run it
came from if you did your training on Azure machine learning.
How we're expanding is we're enabling you to actually
provide a framework that you chose to have your model in,
as well as a framework version,
sample input data, sample output data,
input schema, and output schema.
>> So if I'm understanding this right,
you're saying I can basically
describe what I want to go in a model?
Because to me, a model is
just a glorified function that we're too lazy to write it,
we just given data and it figures it out, right?
>> Yes.
>> So basically, all we have to do is tell
it what goes in and what goes out,
and I don't have to write a scoring file
anymore, is that what you're saying?
>> Currently. It's for certain frameworks.
>> I got it.
>> We're introducing this service called No code deployment.
>> Okay.
>> Now basically for ONNX Models,
Auto ML models, scikit-learn and TensorFlow Keras models,
we are able to actually ask you to tell us what framework it is,
what version it is and we'll go
ahead and actually create a driver file basically,
a scoring file to run your model,
create the image, and deploy it out for you.
So all you're doing is providing your models
and information and you just let it go.
>> I see. So it's basically for
certain styles of model which are very specific.
Like, for example, if you're making a gun that's
kind of a weird scenario where you
might need to write your own driver file,
but for scenarios that are standard like scikit-learn,
you said TensorFlow and ONNX.
>> ONNX and auto ML models.
>> Auto ML models, it'll basically all you have to do is
have a model and you can literally straight deploy out.
>> Pretty much, yeah. We have
a very simplified same way that you deploy models now,
you just put in less stuff.
>> That's nice. So can we take a look?
>> Yeah, let's take a look here.
So who's back here.
So basically here is the new Azure Machine Learning UI.
If you haven't checked it out, please check it out.
>> ml.Azure.com.
>> Exactly. So basically what I've done here is I've
registered a model before and right here,
I'm defining the model.
I can say my framework, the framework version.
If you're doing an experiment on Azure machine learning,
we can go ahead and store and extract
that information and put it in your model attributes.
>> That's awesome. So there's traceability
from where your model came from.
>> Exactly.
>> That's cool.
>> Yeah. So if we go back here,
all right, register a new model.
This is pretty much what it looks like.
>> So this is new.
>> Yes.
>> Because before I know I wasn't able to upload a model,
I basically had to do it in code, right?
>> Yeah. So now you can go ahead and upload
in browser model and register it.
So now if we want to actually
look at the code for a simplified deployment,
all you're pretty much changing in your natural flow
of let me pull a workspace which is basically your project.
Let me go ahead and train my model.
Now, when I register,
I'll go ahead and do what I just showed you in the UI.
Then when you deploy it, you just have
this simple line of code right here.
So I'm deploying an AKS here.
Define my deployment target,
here is my model,
I name it, we're good to go.
>> That's nice. The thing that's interesting
to me is how much more simplified
it is especially with things that we already know about.
Can you talk about this in the larger context of MLOps?
Because I've heard MLOps and I want to
hear what that means and how this fits into it.
>> So basically, we have
multiple different personas going out and deploying models, right?
>> Right.
>> So you have to understand
certain contexts depending on
what deployment pipeline or which stage you are at.
>> Sure.
>> So as data scientists, if I don't work in Python,
I want to go and learn a bunch of Python,
write these driver files.
So what this pretty much helps
you do is you start getting those models
out quickly without having
those intermediate stages of let me create the image,
let me learn Docker, let me understand the environment or
what is the optimal of resource to deploy
my environment, our deployment model.
So what we're doing with this is creating a baseline
to get the optimal resources,
optimal environment to be able to run your mouse.
Then you pass on those information to your DevOps engineer,
DevOps pipeline and it already has here's the optimal CPU,
here's what the MLOp should look like,
let me go ahead and do a deployment pipeline
validating all of these,
and I have that information from the model registry.
>> That's pretty cool. So MLOps is like DevOps but for ML, right?
>> Amazing.
>> This is pretty cool. Thank you so much.
Anything else you want to add to this?
>> No, I am just looking
forward to getting runs and put them feedback on this.
It's a really exciting new feature,
and I think it'll really help
a lot of the data scientists or engineers
out there that are trying to just get their models deployed.
>> This is awesome. So today we've learned about
no code deployment of models that you can literally upload today.
Thank you so much for watching
and we'll see you next time. Take care.