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JONATHAN WEXLER: Hey, everyone.
This is Jonathan from SAS Machine Learning and Artificial
Intelligence.
SAS visual data mining and machine learning,
affectionately known as VDMML, provides
an automated, open, and most importantly
transparent and explainable experience for both business
users and data scientists.
Unlike many of our competitors, VDMML is not blackbox.
Today, I'd like to show you how SAS automation is transforming
the machine learning landscape.
Let's take a look at VDMML in action.
I've logged into VDMML, and in front of us,
we have a dynamic pipeline.
This pipeline was automatically generated
based on the data I loaded to the system.
Now, the types of techniques that are available to you
are feature engineering, dimension reduction,
feature extraction, to many different machine learning
techniques.
The system will find the most optimal solution
based on your data.
How did we get there?
Through the ability of adding a pipeline.
I can either add a pipeline from the SAS Exchange.
Over the years, you've seen how the SAS Exchange provides you
with the ability to add in templates from other users,
so you can add in best practices from one of your data scientist
colleagues, or you can even add in best practices from SAS.
SAS provides out-of-the-box templates for you to use inside
your analysis.
The Exchange is a wonderful place for collaboration.
What we've added here is the ability
to automatically generate pipelines.
So once I hit the OK button, an intelligent process
is underway.
The system, depending on the type of data that I load,
will intelligently determine what
the right set of transformations--
the optimal set of features to help predict my outcome.
As it's going through this process,
it's continually benchmarking.
So as it finds a set of transformations,
a set of features that it wants to use in its models,
it will continue to benchmark and then build another model.
So for example as its determining
the right set of features, it will add
in a gradient boosting model.
It will then add in a forest.
And it will optimally tune those features
to find the best solution.
And this is an iterative process.
So as it finds an algorithm that helps predict the outcome,
it may go back and try additional features.
So again, this is a continuous process
that will run through hundreds, if not
thousands, of permutations to help find the optimal solution.
And as this is running through, you're
presented with an automated status.
So the status that you are presented with is real time.
It gives you an indication of where it is in the process.
So it looks like it's completed.
Let's take a look at the results.
As we can see here, it automatically drew a pipeline.
Now, there's different types of transformations
that it generated, different algorithms.
So for example, it ran decision trees, and in this case,
logistic regression.
And you'll notice there that it ensembled
a handful of the models.
It didn't just ensemble everything.
It determined the smart way to ensemble
to find the best solution.
Let's take a look at what the system will
generate once it's completed.
If we go back to my original pipeline,
we can see here that the system ran
through different permutations.
And the model that it chose was an ensemble model.
And if we take a look at the results,
you're presented with different types of score code.
You're presented with assessment information.
Typically, when a data scientist is analyzing their results,
they're very comfortable with what a lift chart.
Means but if you're a business analyst,
it's possible that you don't understand
what cumulative lift is.
You don't understand what an ROC plot is.
So as I click through the system,
there's little info helpers.
So for example, I clicked on Cumulative Lift.
And you'll notice through the use of natural language,
the system presented a natural language understanding.
So for example, you can see here that the VALIDATE partition
has a Cumulative Lift of 1.84 in the top decile, for example.
But this helps explain to me why this particular metric was
important and how to interpret it.
It's dynamic.
So for example, if you have new data
and the system has determined a different model,
has run through different properties,
the explanation will be different.
So as I'm clicking throughout the system,
I'm provided with different explanations.
Model interpretability is critical
when understanding the validity of a model.
For those of you out there that are not
familiar with such techniques as LIME, ICE, partial dependency,
Kernel SHAP--
these are very popular methods in the market
to help describe from a surrogate perspective what
the important factors are in your model.
So inside the system, you're presented
with partial dependency plots.
So if I'm a business analyst, it's
very easy to understand from a "what if" perspective, what
happens if my behavior score changes?
My prediction changes accordingly.
For example, if I click on a different product--
you can see here that if I have the lavender product,
I have the highest probability.
And again, if I'm not sure how to interpret this information,
the system provides me with a real time
readout in interpretability.
We also have many other advanced techniques,
from LIME, ICE, Kernel SHAP.
These allow you to go even deeper
into individual observations.
So again, I don't need to be a data scientist in this case
to understand how to interpret these models.
It's important to note that the system provided
an automated readout, or an automated pipeline,
for me to be able to interpret or to build
the most accurate model.
But I mentioned that this is not blackbox,
and this is also customizable.
So you have the ability, for example, to add in, let's say,
SAS code.
If I wanted to amend my analysis or extend it,
I can add in additional SAS code.
I can also add in open source code.
So the system is also open to different languages,
from Python to R. So if you have other data scientists
on your team and they want to amend the analysis,
they want to add to the analysis,
they can bring in other open source techniques.
Once you're, quote unquote, "done,"
this could have taken days.
This could have taken weeks, because this
is an iterative process.
Perhaps there is approval processes
that you want to run through.
Model deployment is critical.
And the system certainly has many ways to deployment models,
from one click, to REST APIs, to in-database, in-Hadoop
processes.
But let's say you're a business analyst
and you want to be able to explain this
to the rest of the business through the use
of automated Insights.
Automated Insights allow you to be
able to communicate this information to a wider
audience.
So when I click on Insights, you'll
notice that the system dynamically
generated what I like to call-- it's almost a book report.
This is a summary of everything that I've done today
inside this analysis.
And through the use of natural language,
this gave me an indication of the most important factors.
But this also has natural language built in.
So again, if I'm not familiar with any one of these results,
I can just click my little info helpers.
So again, this system is intended for not only business
users, but it's also intended for data scientists
to work together with these business users.
So I hope you enjoyed this quick tutorial on SAS automation.
It's a really exciting time to join in and use VDMML.
You can be a data scientist and write the most advanced machine
learning models, but you can also be a business user,
and through the use of SAS automation,
you can take advantage of either SAS best practices, or even
best practices from your colleagues.
And most importantly, you can communicate your findings
and deploy your models to production.
Thanks for joining.