Do what you do best…
summary
Claims a practical stack: deploy a full SAS Viya instance on Azure Marketplace in under an hour with JupyterHub, support for SAS/Python/R, and built-in model ops. Shows concrete tooling: 60+ native data connectors, data quality profiling, visual analytics, model repository, automated monitoring and privacy flags. Cites the stark reality that 80% of machine learning models never reach production, demonstrates a 45–60 minute cloud install and a pay rate near $0.55/hour, and presents the system as working end-to-end.
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chapters (20)
- 0:00Intro
- 0:15Introducing SAS
- 0:54What will he talk about
- 2:01Introductions
- 2:18About SAS
- 5:43History of SAS
- 7:56Customers
- 8:11Analytics
- 9:04Data
- 12:39Vision
- 14:50Data to Set
- 15:20Transparency and Trust
- 17:51Saving Life
- 18:40Computer Vision
- 19:47Full Analytical Life Cycle
- 22:44Data Quality
- 24:54Visualization Reporting
- 25:59Model Pipeline
- 27:30Model Repository
- 29:06Azure Marketplace
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