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Advanced Analytics
Natalia Summerville 3
Maximize product quality with Optimization and Machine Learning models

Machine Learning models are becoming widely used to formulate and describe processes’ key metrics across different industry fields.  There is also an increasing need for the integration of these Machine Learning (ML) models with other Advanced Analytics methodologies, such as Optimization. Specifically, in the manufacturing industry, SAS explored state-of-the-art science

Advanced Analytics
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Automated linearization in SAS Optimization

Linear programming (LP) and mixed integer linear programming (MILP) solvers are powerful tools. Many real-world business problems, including facility location, production planning, job scheduling, and vehicle routing, naturally lead to linear optimization models. Sometimes a model that is not quite linear can be transformed to an equivalent linear model to reduce

Advanced Analytics
Joe Mueller 0
SAS Conversation Designer: interacting with APIs

By making requests through API calls you can expand the functionality of the bots you make with SAS Conversation Designer; allowing your bots to query external sources for up-to-date information, score a model, and many other possibilities. This is very beneficial as SAS Conversation Designer is included in many offerings of the modernized SAS Viya platform, meaning you can easily create bots that are integrated with the other services of the SAS Viya platform or third-party services.

Advanced Analytics
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Mathematical optimization at SAS

Note from Udo Sglavo on mathematical optimization: When data scientists look at the essence of analytics and wonder about their daily endeavor, it often comes down to supporting better decisions. Peter F. Drucker, the founder of modern management, stated: "Whenever you see a successful business, someone once made a courageous decision."

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