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
Monthly Archives: March, 2021
SAS Conversation Designer is available with every offering that also includes SAS Visual Analytics. Users can easily access Visual Text Analytics capabilities from SAS Conversation Designer with minimum additional configuration.
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
A Deep-Q Network (DQN) is a reinforcement learning technique that attempts to model the actions that perform best in each state in real-time.
The recently released 2021 Gartner MQ for Data Science and Machine Learning contains a wealth of information and here are my takes on key market trends from that report for data scientists. This evaluation features SAS Viya with its SAS Data Science offerings.
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.
Note from Udo Sglavo: In our peace of mind blog series, we documented areas of analytics that are either evolving or not necessarily in the standard toolset of data scientists. We looked at causal modeling, network analytics, and econometrics, to name a few. With this blog post, we would like