Course Overview:
Artificial Intelligence(AI) and Edge Analytics have been buzzwords for a while. With an exponential increase of the computation power over the last decade, the combination of the two, which essentially allows organizations to run AI workloads at the Edge, is no longer just buzz — it’s happening right now with real, productive use cases.
In this Master Class, you’ll gain a better understanding of Manufacturing computer vision use cases. The speakers will discuss challenges and opportunities, as well as share how to build deep learning-based computer vision models. You will also learn about integration and operationalization of these models into an analytics pipeline. Your key takeaways will include an overview of architectural considerations for at-scale deployment of state-of-art computer vision models.
Presenter Bios:
Daniele Cazzari, Global Lead IoT, Edge and Cloud Analytics Solutions, SAS
Hardi Desai, Senior Associate Machine Learning, SAS
Hardi Desai is an experienced computer vision industrial researcher who has successfully built and deployed deep-learning models for various customers. She supports the Product Management team and customers when integrating SAS AI and ML tools for analytical solutions. She has previously worked with Conduent Labs (formerly Xerox Research Center). She completed her master’s in Electrical Engineering at NC State University with a specialization in computer vision and machine learning. In her free time she enjoys cooking and hiking.
Daniele Cazzari has more than 10 years of experience on Internet of Things edge-to-cloud architecture supporting automotive, manufacturing and insurance customers. He has a proven record of successful projects run at major original equipment manufacturers to develop connected solutions using in-vehicle equipment addressed to major car and truck makers in the EMEA and NAFTA markets. He is currently supporting the SAS-Microsoft partnership as technical lead of cloud-native SAS IoT solutions aiming to simplify data processing and analysis from edge devices.
