LORA EDWARDS: You want the people
making decisions for your business to make the best
decision every time.
The same is true of your models.
You want the highest performing, most accurate models in place
all the time.
My name is Lora Edwards, and I'm going
to show you how to do just that with SAS Model Manager.
We'll look at how you register, score,
publish, and monitor models.
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In SAS Model Manager, I have two main views--
Models and Projects.
In Models, I can see all of the models in my organization.
I can see the types of models.
I can see how many champion models I have,
how often they're being updated, and by who.
When I open a model, I can see all
of the different files that are needed to be
able to reproduce this model.
It's not just the score code.
I can look at the variables that went
into the model and my expected outputs from the model.
Next, let's look at Projects.
I like to think of each project as a business problem.
A great way to use projects is to have multiple data
scientists on the team contributing their solutions
to the same problems.
This way, I can compare their solutions
and choose the model that best fits the problem.
Let's take a look at that.
This is one of my favorite views in Model Manager
because I can get an apples-to-apples comparison
with all sorts of different models.
Here, I'm looking at a Python model and a model
from SAS Model Studio.
I can look at different associated fit
statistics with those and see which
one's going to perform best.
Before deciding that my model is ready to publish,
I would want to run a scoring test.
This allows me to make sure that it
is going to perform as I expected with no errors.
To do that, I'm going to run a new test where
I'm going to choose my model.
And then I'm going to choose an input table.
This will be test data that's representative of my real-world
data that would have missing values or extreme values
so that I can see how my model will perform against--
in real-world situations.
All right, and we have that here.
I can see a profile of that data.
And now I'm ready to run my scoring test.
Great.
So my scoring test ran with no errors.
I'm ready to publish my model.
Here, I choose Publish.
And from the Publish dialogue, I can
see the model that I've chosen and all of the destinations
that I have configured.
So here, I have CAS.
I have Private Docker, and I also have the SAS Micro
Analytic Service.
When I hit Publish, a web service
is being created that I can give to someone
else in my organization to pull into our business application.
Great.
And so my model has published successfully.
Once the model is published, my job isn't done.
The most important thing about models
is to make sure that they are performing
against real-time data in the same way
that we expected them to when we created them.
SAS Model Manager allows me to monitor models over time
and see how well I'm achieving the business
results that I'm looking for.
Let's open this project--
and it's been published for several months now--
and see how it's performing.
On the Performance tab, I can see how my project
is performing over time.
I see here I have the Deviation Index.
This allows me to see which variables are
affecting my models the most.
I can see Lift.
I can see other associated plots.
The different plots on this tab allow
me to see when I may need to retrain or replace my model.
It allows me to make sure that I always
have the right model in place at the right time.
Thanks so much for taking a look at this with me.