New SAS® Viya® enhancements embed transparent AI technology and offer better data governance and improved user productivity
SAS, the leader in analytics, is embedding more artificial intelligence (AI) and automation in the SAS Platform, making it easier for customers to build AI solutions based on proven and trusted analytics. The latest SAS® Viya release also includes significant enhancements designed to maximize collaboration among data scientists and business users, and to improve the creation and deployment of powerful analytics models.
Recent adopters of SAS Viya include McDonald’s, the global restaurant chain; Jack Henry & Associates, Inc., a leading provider of integrated technology services primarily to financial institutions; RevSpring, a technology services company providing communications and payment solutions throughout North America; Elisa, the leading Finnish telecommunications company; Permanent TSB, a provider of retail financial services in Ireland; Government of Longhua District, Shenzhen, China; and Government of Western Australia Department of Communities.
“In today’s analytics economy, enterprises require a powerful analytics platform with end-to-end capabilities, from data preparation and data management to creation and deployment of models,” said Randy Guard, Chief Marketing Officer at SAS. “The SAS Platform and its SAS Viya capabilities provide an open foundation from which data scientists, business users and executives can transform raw data into intelligence.”
Among the enhancements available this June:
Embedded AI and automation. SAS continues to embed AI capabilities and enable automation across the SAS Platform, from data preparation and model building to model deployment. Good data is the bedrock for good analysis, and the new SAS Viya capabilities help data scientists and business users clean, prepare and transform data for analysis.
By automating data prep, users can identify the most significant variables and fields for more focused and effective analysis. SAS Viya also automates model building and deployment, so various models can be tested and the best ones quickly chosen. Since the key to good models is feature engineering, SAS will add new feature engineering capabilities to improve model accuracy.
For natural language processing, SAS is automating sentiment analysis and document classification using recurrent neural networks (RNN).
More transparency. With more advanced machine learning and deep learning methods, it is imperative to support the principles of fairness, accountability and transparency in AI and machine learning.
