I’ve been giving presentations about SAS® ViyaTM for a couple of months now, and the reactions have been positive. I’m part of a much bigger cast of speakers at SAS who talk about the new analytics platform with key customers and analysts. While some presenters focus on the overarching benefits or the integration with previous versions of SAS, I get to focus on hands-on demonstrations. My style is to show SAS Viya technology in use, which helps attendees understand the different user experiences.
The demonstrations I show use anywhere from 90,000 to 2.5 million observations. Sometimes I’m looking for fraud or sometimes I’m looking for opportunities to reduce attrition. No matter what business problem I’m trying to solve, I like to show four different ways to tackle the problem, depending on the user persona and skillsets.
First, I show SAS® Visual Statistics. This is a suitable interface for business analysts and citizen data scientists. I can point and click to do a logistic regression and find an answer. Or, I can start to explore my data with SAS® Visual Analytics before I do any modeling.
Running a logistic regression in SAS Visual Analytics (click to enlarge).
Next, I show SAS® Studio, where you can also point and click, or you can program SAS code. You can do both here, or toggle back and forth, which provides a lot of benefits when reviewing how your code works. Here, I run a logistic regression, write the procedure and use PROC LOGSELECT, which is the logistic procedure in SAS Viya. I also point out all of the exploratory and descriptive tasks and procedures you can use before fitting your model.
SAS Studio shows the selected model and the code side by side (click to enlarge).
Then, I tab over to Jupyter Notebook and show the CAS Python API. (CAS is short for cloud analytic server.) I can write Python code that calls specific CAS actions, like calling the logistic action set or performing model assessment. The CAS Python API is not yet released but will be available later this year.
Performing a model assessment from inside Jupyter Notebook.
Finally, I like to show these prepackaged predictive models that run SAS through APIs. Using analytics as a service by SAS, these APIs can be used to embed logistic regression or other modeling techniques into any other application.
APIs are portable pieces of code that can be easily combined and stacked together to enhance other applications or websites (click to enlarge).
I use the same data for all of the examples and because the same CAS action set is used for analysis you get the same results. For each one, SAS Viya goes right to the data and lifts it up into memory. I can perform my analyses quickly and nimbly, and when I’m done, the data dribbles back down to its initial location instead of proliferating copies of the data on hard disks.
Not only is it quick and easy to get complex answers – but you can get them in whatever way feels most comfortable to you.
Which of the four do you prefer? And what ideas do the different options inspire in you?
Choose your own adventure with SAS Viya was published on SAS Users.