>> You're not going all miss this episode of the AI Show.
Where we learn about, automated machine learning,
how to build AI with AI and One-Click Deploy to boot.
Make sure you tune in. We'll see you there.
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>> Hello. Welcome to this episode of the AI Show.
We are going to talk about automated machine
learning with PSI. Tell us who you are and what you do.
>> Hi. I'm a Program Manager in the Azure Machine Learning Team,
and we're here to talk about automated machine learning.
>> Fantastic. So 30 seconds or less,
what's automated machine learning and why should we care?
>> So automated machine learning automates part of
the process of the machine learning workflow,
so you don't have to worry about
all the bits, the technical stuff.
It really democratizes machine learning
for more than just data scientist.
More for like if you're data analyst,
if you're data engineer or if you're a business analyst,
you can leverage the power of machine
learning by using automated ML.
>> That's awesome. Well it's a big promise.
Can we dive in and take a look?
>> Sure.
>> Well lets do it.
>> So you can see that here I'm in
my Azure Machine Learning studio
and I'm in automated ML section under Author.
I'm going to create a new automated ML Run.
So our first step is we select the data set.
I already have one that I
uploaded but I can easily create a new one,
either from local files or from the data store from wav file.
So let's select the one that I've already created.
The next step would be to provide an experiment name.
So I can again select one that
I've already created or I can create a new one.
Then, I need to select the target column,
which is essentially what do I want to predict.
>> Fantastic.
>> So in this case, it's the column name y,
which is whether the customer
created the deposit to the bank or
not based on the marketing that he'd got.
The third step would be to select the "Compute".
The Compute would be the compute power
that the training job for the model will actually run on.
So I already have one created but if I don't have one,
automated ML will help me create one really fast on the fly.
I just need to give it a name,
hit "Create" and that's it, like it takes a minute.
Then the next step would be select the task type.
So in this case it's a classification because I want a yes or no,
on whether an effort succeeded or not.
We also support regression and time series forecasting.
If I'm more versed with machine learning,
I can go into "Additional settings"
and set some more advanced settings,
so if I want early stopping
or use a different primary metric to analyze the model.
Then, another thing that I can do is go into the "Featurization
settings" and exclude some of the columns,
some of the features if I don't want them to be
included or if I want to change the feature type,
the strategy that I want the featurizers to work on.
So that's it. Basically, I'm ready.
I hit "Create" and this will spin up the run.
That's basically all of it.
>> That's pretty amazing. So you basically select the data set,
you configure the data and
then you configure the run and then you're done.
>> Yeah.
>> That's awesome. Then it'll run for however long you want it to.
>> Yeah. So this can take a while
because it will run through hundreds,
probably thousands of different combinations of models.
Eventually, what we'll see when the run is done
is that we'll see that it's completed
and there is a recommended model.
So this is the model that automated e-mail said, you know what,
this is the best one because it reached
95.4 percent in the metric that we have selected.
From here, I can also analyze all of the models,
that automated ML created,
so you can see that there are
several pages here of models that were generated.
I can go deeper into any one of them
and analyze the generated model.
>> That's cool.
>> So for example I can go and see all kinds
of performance charts for each of the models.
So you can see here there's ROC curves and confusion matrix.
I can also see the explanations for that model.
So I can see the global feature importance
meaning which of the features
were more important to generate that model,
and it's globally important.
I can also go and see
a summary of all the data points that I have,
click on one of them,
and I can get a local feature importance
for that specific prediction.
>> That's cool.
>> The specific row into data.
>> So long as you've done that, because
once the experiments are all,
will give you the one that works the best,
we think well you can go and explore all the other ones,
but one of the important things is once you build the model,
sometimes it's hard to put it into
production. How do you do that here?
>> Yeah. So automated ML helps with that too.
Once I'm satisfied with a model,
it doesn't have to actually be like the one that was recommended.
I can select any one of them.
I hit the "Deploy" button,
I give the deployment a name and that's it.
Basically all the scripts,
everything will be automatically generated for me.
I hit the "Deploy" button and that's it.
>> This is few minutes.
>> I will have a deployed model.
>> This is amazing, because as a data scientists,
it's always good to start with
the baseline model that you know works,
and automated machine learning looks like it
does that and allows you to deploy it as well.
>> Yeah.
>> Well thank you so much for spending some time with us.
>> Thank you.
>> Thank you so much for watching.
We're learning all about how to use
automated ML quickly and easily as well as One-Click deploy.
Thanks for watching and we'll see you next time. Take care.
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