>> You're not going to want to miss this episode of the AI Show.
We talk about the Azure Machine Learning Designer,
some of the new capabilities.
It's pretty cool. Make sure you tune in.
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Hello and welcome to this episode of the AI Show.
We're going to talk about something called
the Azure Machine Learning Designer.
I've got a special guest with me, Lu.
How about you tell us who you are and what you do, my friend.
>> Hello, everyone. My name is Lu Zhang.
I'm a Program Manager from Azure Machine Learning Team.
>> Tell a little bit about what the Designer
is for those that are maybe not familiar.
>> Sure. Designer is basically the drag and
drop visual interface that you can easily build,
test, and deploy a production-ready model
with low code or no code.
>> For those that know about it,
what's new this time around?
>> Yes. For Designer,
this time, there will be a new layout.
There will be a couple of
new algorithm, like image classification,
like recommendation, and we also have
improved debugging and troubleshooting experience for you.
>> I love knowing about stuff,
but I'd love to see if you have
something to show. Do you have something to show us.
>> Sure. I can show that.
What you see here is the homepage
of Designer in the Machine Learning Studio.
As you can see from the homepage,
there are a bunch of examples from
the image classification to recommendation to linear regression.
Also, you can even use
custom Python script to build your own model.
What we are going to show today is how you can use
the new simple image classification using
the DenseNet to build a classification model.
As you can see, this is the sample pipeline
and it starts from image animal dataset.
It convert to the image directory with a bunch of transformation,
and then you use DenseNet to train a PyTorch model.
After that, you should be able to
score the model and evaluate that.
I'm going to submit a pipeline run on top of that.
I can "Select Compute Target".
Please note, because it's using the DenseNet,
you will be using that GPU cluster
and then I will submit a pipeline run.
>> As you're submitting, there's just a couple of questions.
Basically, the Designer lets you create a flow for
what I would call a machine learning pipeline, is that right?
>> Yes. The back-end is the machine learning pipeline.
>> I see. When this is actually running,
it's basically going to run everything in parallel that it can and
everything in series that it has to. Is that right also?
>> Yeah. The pipeline is basically
the orchestrator that will decide when to run which node.
>> That's pretty cool. How long does
something like this take to train?
>> It depends on the data, the volume,
and which skill you chose for the compute target.
For this example, because we chose the GPO,
it's going to take a couple of minutes to finish run.
>> Then the other question is, it looks like
you just went from the sample and just drew out this huge thing,
but if you start from a blank canvas,
what things can you drag onto that Designer canvas?
>> Sure. You can check the left side, the Azure Library.
We have the dataset,
which is what you already registered using
the dataset capability in Azure Machine Learning.
We also have a bunch of modules available from
data transformation processing to
feature selection to machine learning algorithm.
You will have basic regression, clustering,
and you will also have for text analytics,
computer vision, and the recommendation.
Basically, already a rich set of
built-in modules you can drag and drop into the canvas.
>> Maybe I'm wrong, but I'm guessing you already
have one of these runs that is done already,
so we don't have to watch it. Is that the case?
>> Yeah. I will share the one which already has finished running.
In this example, actually,
I'm using a real registered dataset.
This comes from the laboring project,
and we use that as the input to the pipeline.
We then use custom module that convert
it to the image directory to the format we want.
After that, there are a bunch of transformation,
and automatically, you will finish running to get the result.
I can show the results, what you will see.
For example, for this score image model,
you can right-click to visualize for each of the image,
what is the probability that you assign to each class.
For this one, you are telling which one
is bookstore versus warehouse versus subway picture,
and the score label will tell you what the actual model predicts.
>> As I'm looking at it, let's just say you're
happy with the model that's been built.
Is there an easy way to deploy this?
>> Yes, definitely.
From the Designer portal,
you should be able to finish running.
You should be able to convert that into an inference pipeline,
and that will automatically generate
a real-time inference pipeline, something like this.
You see, we are now taking
the web service input as the input to the train model,
and then you will get the prediction result
returned from the REST API.
>> I see. So not only do you have
the training pipeline represented in the training model,
but you also have the inference, because obviously,
when you're training versus when you're doing inference,
it's a similar thing,
but it's still different,
and you can represent both in Designer, is that right?
>> Yes. This is automatically generated,
so you don't need to worry about how to
build an inference pipeline from scratch.
>> Well, this is quite impressive.
What does it look like when you actually deploy the endpoint?
>> You can just click on the "Deploy" button
after you got the real-time inference pipeline,
and then you can choose Azure Kubernetes Cluster to deploy it.
That will take a few minutes to finish deployment.
But once you finish that,
you should be able to go to
the Endpoint page to check the actual deployment.
I'm going to show the one I have deployed before.
You will see the details of the real-time endpoint.
The REST endpoint can be easily copy-paste,
and what's more interesting is you can select the image from
your local computer to test what it means to score the model.
I'm going to use the sample here,
and you will see the actual prediction to verify your model.
Once you want to go to the production,
you will also get a sample code,
how to consume that in your application using C#,
Python or R, that will be very handy
for you to integrate into your application.
>> This is all pretty amazing.
It goes from not only the beginning of training,
but all the way to that inference time and even testing.
Where can people go to find out more about this?
>> They can find it from our documentation site
from the Azure Machine Learning official website.
All the resources will be available there.
>> Awesome. Well, thank you so
much for spending some time with us,
Lu, and thank you so much for watching.
We've been learning about some of the cool new features
in the Azure Machine Learning Designer.
Make sure you check it out. Thanks for watching,
and we'll see you next time. Take care.
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