SAS partners with NVIDIA on deep learning and computer vision

SAS is partnering with NVIDIA to help businesses bring artificial intelligence (AI) into their organisations. The companies are collaborating across machine learning, computer vision and natural language processing, with NVIDIA GPUs and CUDA-X AI acceleration libraries, to support the core elements of SAS’ AI offerings – leading to faster, more accurate insights.

 

“Analytics is the core of AI,” said Laurie Miles, Director of Analytics at SAS UK & Ireland. “SAS is constantly looking at ways to improve its customers’ analytics capabilities for successful AI deployments. This partnership with NVIDIA combines our software expertise with their powerful GPUs, creating world-class engines for rapid, accurate AI operations. AI is opening up huge possibilities for organisations of all varieties, and SAS is at the forefront of innovation in this important field.”

 

For industries such as health care, life sciences, manufacturing and financial services, artificial intelligence delivers significant value. Here are some examples of how SAS and NVIDIA are helping customers accelerate their AI efforts:

 

●        Health care providers using object recognition to identify malignant versus benign cancer cells.

●        Manufacturers using computer vision to find defects before products leave production lines.

●        Financial institutions saving trillions of dollars with fraud detection.

 

“Our collaboration with SAS will help enterprise customers extract the true value of AI for their company,” said Ian Buck, Vice President and General Manager of Accelerated Computing at NVIDIA. “With NVIDIA technology, businesses will be able to accelerate their entire data science workflow to innovate, add new services and increase profitability.”

 

With expanded NVIDIA GPU support across SAS^® Viya^® – including products such as SAS Visual Data Mining and Machine Learning and SAS Event Stream Processing – customers can take advantage of high-performance AI capabilities including image classification, object detection, speech-to-text, image recognition and sentiment detection. The companies are also pushing deep learning and decision-making capabilities to edge devices, which will drive greater IoT opportunities. For example, equipping an edge device like a commercial drone with AI technology will give it the ability to handle everything from infrastructure monitoring to predictive maintenance for industrial plants. With the GPU and analytics built into the drone, analysis can be performed where the data resides. Analysing data in real-time results in faster and more accurate decisions.