This article is the first in a series of three publications covering REST APIs and their use in, and with SAS. Today, I want to cover a basic example using SAS Viya REST APIs to download an image from a report in SAS Visual Analytics.
How do you deploy your model so that business processes can make use of it? This post explores how SAS Viya applications can directly add models to a model repository, and specifically focuses on how to deploy them with SAS Model Manager to Hadoop.
In addition to the many CAS actions supplied by SAS, as of SAS® Viya™ 3.4, you can create your own actions using CASL. Learn how in this post.
With SAS Data Preparation and SAS Decision Manager, you can perform out-of-the-box column and row transformations to increase your data quality and build the foundations for data-driven innovation. This blog will discuss how you can leverage SAS Decision Manager to enrich data when preparing it through SAS Data Preparation.
Did you know that you can now chat with SAS Technical Support? Technical Chat enables you to quickly engage with a knowledgeable consultant when you have a SAS question or need help with troubleshooting an issue.
Deep learning (DL) is a subset of neural networks, which have been around since the 1960’s. Computing resources and the need for a lot of data during training were the crippling factor for neural networks. But with the growing availability of computing resources such as multi-core machines, graphics processing units
Self-service BI applications make gaining insights and decision making faster. But they've also generated a greater need for governance, including understanding the data lifecycle. You can find out where your data comes from with SAS Lineage Viewer. The post tells you how.
Do you work with custom polygon maps in SAS Visual Analytics? Read about a few "gotchas" to help you troubleshoot some common issues you may encounter.
Demo SAS Visual Analytics live on your mobile device to your team across the world. Read this post for the details.
How to extract driving patterns by using smartphone sensors -- especially the accelerometer. With accelerometer readings in hand, you can use spectral analysis and other techniques to decompose events, and machine learning to match differentiate from patterns of risky drivers.
