This is the second post in my series about a computer vision project I worked on at SAS. In my previous post, I talked about my initial research and excitement for the project. In this post, I’ll talk about how I refined my goals and got started with the project to segment liver tumors in 3D CT scans.
Some say that the insurance industry is a long, quiet river on which stately steamships cruise. Others say it is a shark tank where only the strongest survive. Which is right? The answer is both. The insurance market is clearly mature, with a limited scope of action for individual players. There is, however, no question that merciless predatory competition is taking place, probably precisely because of this saturation.
Modern customer intelligence systems give managers the ability to track key success factors. This means they can make better decisions about the allocation of physical and financial resources and improve strategic planning. This blog post builds on previous articles about how customer intelligence can support marketing and discusses its integration into a broader ecosystem.
Multi-tenancy is one of the exciting new capabilities of SAS Viya. Because it is so new, there is quite a lot of misinformation going around about it. I would like to offer you five key things to know about multi-tenancy before implementing a project using this new paradigm.
Analytical models are created in a data lab or sandbox where they are refined and tested under controlled conditions. Once the model is sufficiently mature, it is released into the production environment, where it spends the second phase of its life processing full volumes of data on a daily basis for automated decisions at scale. At some point, the model may be returned to the data lab for re-evaluation and retuning, or perhaps retirement and replacement.
Each day, more than 130 Americans die from opioid overdoses. Combating the opioid epidemic begins with understanding it, and that begins with data. SAS recently partnered with graduate students from Carnegie Mellon University (CMU) 's Heinz College of Information Systems and Public Policy to understand how data mining and machine learning capabilities can be used to generate insights from public Medicare data, both structured and unstructured (i.e. text data).