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.
Chatbots offer another user-friendly conversational interface to the entire SAS Viya ecosystem, bringing together reporting capabilities, analytics and artificial intelligence.
Often, when a cybersecurity incident occurs, the clues to how it happened and who caused it are hidden in network data. In the example discussed here, data scientists were asked to identify who caused a global internet outage by examining a large graph of network data with data visualization. This
The first principle of analytics is about bringing the right analytics technology to the right place at the right time. Whether your data are on-premises, in the cloud, or at the edges of the network – analytics needs to be there with it. Being true to this principle means we
SAS' Véronique Van Vlasselaer reveals why managing model performance is as important as putting them into production.
What does the AI enterprise of the future look like? That’s a tough question that I’ve been asked to consider, along with a distinguished panel at Valley ML AI Expo 2020. The title of the panel is, “Life, the Universe and the AI Enterprise of the Future.” Based on an initial chat with panel chair Gautam Khera, I’ve written up some possible topics we’ll be covering on the panel. Consider
Through hyperparameter autotuning, you can maximize a model's performance without maximizing effort. While SAS searches the hyperparameter space in the background, you are free to pursue other work.
Deep learning is an area of machine learning that has become ubiquitous with artificial intelligence. The complex, brain-like structure of deep learning models is used to find intricate patterns in large volumes of data. These models have heavily improved the performance of general supervised models, time series, speech recognition, object
Computer vision can augment radiologists and make the image interpretation process cheaper, faster and more accurate. The ultimate goal is to achieve a better patient outcome facilitated by the use of computer vision.
Neural networks, particularly convolutional neural networks, have become more and more popular in the field of computer vision. What are convolutional neural networks and what are they used for?
