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Engineers at Google proposed the idea of the mannequin card in 2018 to extend ML transparency and create a technique to talk utilization, ethics-informed analysis, and limitations. The mannequin card has since seen varied ranges of adoption, like this instance for a Llama 3 mannequin in Hugging Face. And as of the SAS Viya 2024.07, a mannequin card is now accessible in SAS Mannequin Supervisor.
The mannequin card in SAS Viya was designed like a diet label for AI fashions. What units the SAS Mannequin Card aside from earlier mannequin playing cards is the usage of descriptive visuals, to make mannequin playing cards accessible to all personas concerned within the analytics course of, together with information scientists, information engineers, MLOPs engineers, managers, executives, threat managers, enterprise analytics, end-users, and every other stakeholder with entry to the SAS Viya atmosphere. And SAS is so assured in our mannequin card design, we’ve even offered it earlier than the US Congress. Moreover, most of our mannequin card populates routinely because the mannequin is developed, managed, and deployed in SAS Viya. With AI, belief is every thing. With SAS Viya, it takes 5 minutes to construct a mannequin card that communicates efficiency, equity, and transparency with AI.
SAS is taking a considerate strategy to the way it helps prospects embrace AI, specializing in the sensible realities and challenges of deploying AI in actual trade settings,” stated Eric Gao, Analysis Director at analyst agency IDC. “Mannequin playing cards shall be helpful for monitoring AI initiatives and selling transparency.”
The mannequin card has 5 sections: Overview, Mannequin Utilization, Information Abstract, Mannequin Abstract, and Mannequin Audit. The Overview part is the very first thing you see while you open a classification or prediction mannequin in SAS Mannequin Supervisor. This part offers an at-a-glance abstract of mannequin well being and significant components so a call maker can shortly perceive if their mannequin is an effective candidate for deployment, wants work, or must be changed as their manufacturing mannequin.
The next sections permit groups to dig deeper in order that they will pinpoint points surfaced within the overview and decide what areas should be addressed. The Mannequin Utilization consists of handbook fields that spotlight the supposed utilization, anticipated profit, out-of-scope use instances, and limitations of the mannequin. This offers important info for making certain that the mannequin is getting used as designed.
The Information Abstract pulls info from SAS Data Catalog to offer metrics concerning the coaching information. This contains details about information completion, info privateness classifications, measurement, and standing. With the information abstract, customers can shortly discover if their mannequin leverages delicate or personal variables in addition to dive deeper into the information used to coach the mannequin.
The following part is the Mannequin Abstract, which graphs varied metrics of mannequin efficiency on the coaching, testing, and validation splits. The Mannequin Abstract additionally visualizes mannequin equity and mannequin interpretability metrics, if accessible.
The ultimate part is the Mannequin Audit tab, which summaries the mannequin’s efficiency on the newest efficiency monitoring information. Customers can set thresholds in SAS Mannequin Supervisor utilizing the Key Efficiency Indicators (KPIs), that are used to find out if the mannequin is at present performing as anticipated. When a mannequin decays, it turns into much less efficient as a predictor. Because of this organizations should act shortly to place a brand new mannequin in manufacturing to make sure they will make correct choices utilizing their fashions.
Mannequin playing cards shall be freely accessible on SAS Viya platforms operating the 2024.07 launch (or later) that embrace SAS Mannequin Supervisor. In case you have the SAS Viya, SAS Viya Superior, or SAS Viya Enterprise foundational choices, the mannequin card will turn out to be accessible in your subsequent replace. Organizations adopting month-to-month updates will be capable to choose up the mannequin card instantly, whereas groups on the LTS path can choose up the mannequin card of their subsequent Lengthy Time period Steady launch.
Don’t have entry to the newest SAS Viya atmosphere? Not an issue! We have now just a few paths accessible for attempting the brand new mannequin card:
Benefit from the free SAS Viya trial. The free trial is on the LTS launch cadence, so chances are you’ll want to attend till Winter for Mannequin Playing cards to seem, however this can be a free two-week trial of the SAS Viya Enterprise providing. You may add a number of customers, so you’ll be able to create and share the mannequin card along with your staff. You can begin this trial at any time and the trial atmosphere takes care of deployment and infrastructure of the SAS Viya platform.
Want extra time? SAS Viya on the Azure Market contains every thing within the SAS Viya foundational providing and allows you to pay-as-you-go. And identical to the free trial, you can begin at any time with out worrying concerning the platform deployment or infrastructure. This providing is up to date month-to-month, so you’ll be able to check out the mannequin card instantly!
The SAS Hackathon is simply across the nook and offers a possibility to leverage the newest tooling on SAS Viya to unravel an issue of your selection. Collaborating within the month-long Hackathon is free, however you do have to register by August thirtieth.
After getting entry to the newest SAS Viya atmosphere, you’ll be able to learn extra about how every part of the mannequin card is accomplished on this article. After getting tried out the mannequin card, we might love to listen to your ideas! Please tell us within the feedback what labored properly, what use instances you tried, and what you want to see in future iterations of the mannequin card as we construct out a base of beneficial greatest practices.
Sophia Rowland, Kristi Boyd, Vrushali Sawant, and Allie DeLonay contributed to this text.
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