On-Demand Webinar

Breaking Down AI Capabilities for IT: What to Know About Computer Vision and GPUs

Hear from an IT director about the technical requirements and major considerations for deploying and supporting computer vision.

 

About the webinar

Computer vision is a capability of AI that changes the way humans process visual data.

By applying automated deep learning models to photographs and videos, we teach computers to interpret and understand the visual world in ways that drive innovation.

The question is, how can you best plan for and accommodate computer vision at your organization?

Tune in as we break down the technical requirements of computer vision through the lens of IT management – and show what to expect when rolling out this critical AI capability.

Join this webinar to learn:

  • Examples of computer vision being used today.
  • Technical requirements of AI software for computer vision.
  • How those technical requirements affect: computing policies and standards, data management, processes, staffing and training, privacy and security, open source software accommodation and cloud-based solutions for storage or processing.

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About the Experts


Jennifer Nenadic, Director of Enterprise Data and Analytics in IT, SAS

With a background in data management and analytics, Jennifer Nenadic has aided SAS’ leaders and external customers in strengthening their business by strategically transforming their current systems into intelligence-generating solutions using advanced analytics techniques and data management best practices. Nenadic has worked with clients across various industries to find creative designs and solutions to meet their evolving business needs. She holds bachelor’s degrees in computer science and textile engineering as well as a master’s degree in advanced analytics from North Carolina State University.


Xiangxiang Meng, Senior Product Manager, SAS

Xiangxiang Meng focuses on deep learning, computer vision, SAS® Visual Statistics, the Python interface to SAS Cloud Analytic Services and other new product initiatives. Previously, Meng worked on SAS® LASR™ Analytic Server, SAS In-Memory Statistics for Hadoop, SAS recommendation systems and SAS® Enterprise Miner™. His research interests include deep learning and reinforcement learning, automated and cognitive pipelines for business intelligence and machine learning, and parallelization of machine learning algorithms on distributed data. Meng received his PhD and MS from the University of Cincinnati.
 

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