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Build End-to-End Models in SAS® Viya® Using the Model Studio

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Join us live for this Virtual Hands-On Workshop to learn how to build and deploy SAS and open source models with greater speed and efficiency. This workshop is offered twice: once on November 8, and again on November 9.

 

The faster and more efficiently you can build machine learning pipelines, the sooner decision makers have the insights they need.

With Model Studio running on SAS Viya, we’ve taken everything that was good about SAS® Enterprise Miner™ and enhanced the interface, speed and scalability.

Designed for distributed computing and using in-memory data and parallel processing, Model Studio on SAS Viya significantly increases the speed with which you can build models on big data. It also requires much less overhead for reading and writing data sets to disk.

In this workshop, you’ll virtually work alongside expert instructors to load data into memory, prepare input variables for modeling and build complex analytics pipelines to demonstrate powerful machine learning models.

You’ll learn how to:

  • Use the SAS Model Studio Graphical Interface for end-to-end modeling.
  • Ingest and prepare data for modeling.
  • Build SAS and open source models in one common analytical environment.
  • Use automated machine learning to jump-start your modeling process.
  • Define and compare multiple analytical approaches through modeling tournaments.
  • Share and explain your project insights.
  • Deploy machine learning results into production.

After this workshop, you will be able to use multiple AI capabilities to confidently solve many of your organization’s problems. Reserve your space for this event here.

Registration is free, and each student will have access to SAS Viya software powered by AWS to follow alongside and practice with your instructor as well as exploring on your own.



Organizer
ChrisHemedinger
Community Manager
Event has ended
You can no longer attend this event.

Start:
Tue, Nov 8, 2022 09:30 AM EST
End:
Tue, Nov 8, 2022 11:00 AM EST
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