Watch this Ask the Expert session to learn how the integration between these SAS solutions helps MLOps engineers, data scientists and business users improve analytics success by working together.
Watch the Webinar
You will learn about:
- How SAS® Viya® supports enterprise team collaboration to efficiently build AI and analytics systems.
- Core functionality of SAS Model Manager for MLOps engineers, including registration, testing, governance, monitoring and integration with solutions such as SAS Intelligent Decisioning.
- Core functionality of SAS Intelligent Decisioning for decision analysts, including rule building, ML/AL implementation, code support, orchestration and deployment.
The questions from the Q&A segment held at the end of the webinar are listed below and the slides from the webinar are attached.
Q&A
After model decay is detected, what are the next steps?
We didn't dive too deep into model decay in this example, but when we develop our machine learning models, they are just representations of a pattern that they detect in the world around us at a specific time point. Our world is ever changing, new things appear all the time, things phase out. So, when our models are trained to work with a specific pattern at a single point in time, it becomes less effective at prediction when that pattern is no longer useful. This is a process called model decay.
Within our SAS model management tool, we have capabilities for setting thresholds for sending tasks or notifications whenever your model decay doesn't meet that threshold, so that users can come in and decide what's next. Do they retrain their models in SAS Model Studio? Do they select a challenger model that looks like it's still performing as well as the champion or better? We do support performance monitoring for multiple models so you can share those challenger models side-by-side those champions. You can quickly see it's time to replace my production model with a challenger or decide to retire the project to build new models. There are a few different options that are available, but we do help with letting individuals know when it is time to address model decay. From there, we can replace production models when we go to deploy; there's a toggle to replace the model or replacing the model with the same name. Or we can just let Crystal know that it's time to swap out that model, as you've seen in the demonstration. We can even very quickly share our models. There's a share button, so we can even share it over teams if I want to speed things along.
What else can I do to ensure my decision flows leverage Responsible and Trustworthy AI best practices?
There are a few things that we can do to better make sure that our decisions fall into that responsible and trustworthy AI category. First, when we are using models within our decisions, we want to make sure that we're working with the data scientists or whoever is developing and maintaining those models. Because a large part of how our decisions depend on the outputs those models. So, we need to make sure that our models are up to date. If there needs to be retraining, retrain the models before our models get stale. In addition, we also want our decisions to be transparent and be able to explain how a decision was made. With SAS intelligent decisioning, there's a few ways we can do this. When we publish decisions, navigate to the Deployments tab to manage the different deployed decisions that we have. We can understand where they've been deployed to, who's deployed them, and the date of publishing. Also, with this, we can get a better understanding of how the decision is running and what it is using to make decisions by easily generating a report that helps us create better transparent decisions.
We can also track decisions with decision path analysis and rule-fired analysis. We can also track decisions with decision path analysis and rule-fired analysis and the output decision variables. With this data we can do analysis to better understand how decisions are made and ensure that we are making responsible decisions.
While developing category models and churn models, do you actually code or is it done by SAS Viya itself?
What's nice about SAS Viya is that it's almost like a choose your own adventure when it comes to how you develop your models. It can be yes code, no code, low code, or somewhere in between. For this particular example, these models that we use were developed using a tool called SAS Model Studio, which is a GUI (Graphical User Interface) based approach where you don't have to do any coding yourself. You can drag and drop nodes into a pipeline, you can use automated machine learning to develop that pipeline for you. You don't actually have to code at all to build out these models we used today. But that doesn't mean you can't code. Users can also code in SAS and Python or R to develop their models. We have a few different options for those users. It is nice and flexible so you can start to bring together skill sets across your organization, whether they can code and enjoy it and just want to stick with that method, or if they prefer to iterate very quickly using a drag and drop tool. There is a variety of different options for how these models can be developed.
How can I learn more?
Sophia: Besides joining us at SAS Innovate, both Model Manager and Intelligent Decisioning are very active on SAS communities. I post under the SAS Model Manager label whenever we have new features, and many of our experts in the community post interesting use cases they come across, and interesting problems they've solved. So, it's definitely worthwhile to subscribe to the SAS Model Manager label on SAS Communities.
Crystal: SAS Communities is always a good one for Model Manager or SAS Intelligent Decisioning. We have some blogs on the sas.com page and then also in SAS Communities. I also suggest taking a look at the previous because we have a lot of good tutorials or how to videos that show you how we move throughout either SAS Model Manager and SAS Intelligent Decisioning. The previous webinars can be especially helpful if you are new to either of these solutions.
SAS Model Manager Communities Link: SAS Communities: SAS Model Manager
SAS Intelligent Decisioning Communities Link: SAS Communities: Decisioning
SAS Ask the Expert Webinars: Ask the Expert - SAS Support Communities
Did you use logistic regression to develop the model?
In this example, the category model is a rules-based text model, and the churn model is a gradient boosting model, but logistic regression models and many other machine learning models are supported.
Recommended Resources
Essential Functions of SAS Intelligent Decisioning
Manage Models in SAS Viya Training
SAS Intelligent Decisioning Homepage
SAS Model Manager Homepage
SAS Viya Homepage
Please see additional resources in the attached slide deck.
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