The model management process, which is part of ModelOps, consists of registration, deployment, monitoring and retraining. This post is part of a series examining the model management process, orchestrated through the Model Manager (MM) APIs. The focus of part one is on model registration, specifically on using the APIs from
Tag: model manager
We will combine three separate SAS Viya capabilities to create an application that can manage multiple models, interpret model outputs, and replace the production model if necessary
Most model assessment metrics, such as Lift, AUC, KS, ASE, require the presence of the target/label to be in the data. This is always the case at the time of model training. But how can I ensure that the developed model can be applied to new data for prediction?
This post describes a fully automated validation pipeline for analytical models as part of an analytical platform, which has been set up recently as part of a customer project.
In total, there are four posts in this blog series, this is the first post describing some basic principles of the DevOps (or ModelOps) approach.
The final phase in analytical model deployment is the perfect unsolved mystery. Why are 50% of analytics models never deployed? And why does it take three months or more to complete 90% of deployed models? What happened or didn’t happen to allow analytical insights to reach their potential? It’s a
My colleagues Tapan Patel, Wayne Thompson & Chris Stephens hosted over 550 live attendees on June 16th for our Data Mining 101 live webinar, part 3 of the Applying Business Analytics Webinar Series. Folks joined from all over North America as well as 32 other countries around the world. Since