If you’re using artificial intelligence to build smart devices such as computer vision monitoring for predictive maintenance, you know that collecting, cleaning, and labeling data is time consuming, tedious, and expensive. Edge Impulse and pretrained models from NVIDIA TAO help get you on the fast track to enterprise-grade production with 100+ NVIDIA-optimized model architectures, like vision transformers and fully attentional networks. Fine-tune the models with your own data, enabling a much faster development process.
Watch the webinar, ‘Fast Track AI to the Edge with NVIDIA TAO & Edge Impulse’, to hear from NVIDIA’s Debraj Sinha (Product Marketing Manager), Edge Impulse’s Jan Jongboom (CTO & Cofounder) and Jenny Plunkett (Senior Engineer) about the latest in edge AI and how to leverage pretrained models from NVIDIA to deploy on edge devices.
Learn how to:
Debraj is a Product Marketing Manager for Metropolis at NVIDIA, focusing on building smarter spaces around the world with vision AI applications. Debraj collaborates with partners ranging from startups to Fortune 500 companies to market AI applications that drive safety and efficiency gains.
Jan Jongboom is an embedded engineer and machine learning advocate, always looking for ways to gather more intelligence from the real world. He has shipped devices, worked on the latest network tech, simulated microcontrollers and there's a monument in San Francisco with his name on it. Currently he serves as the cofounder and CTO of Edge Impulse, the leading development platform for embedded machine learning with 140,000+ projects.
Jenny Plunkett is a software engineer, technical speaker and content creator, working as a Senior Developer Relations Engineer at Edge Impulse. Since graduating from The University of Texas she has been working in the IoT space, from customer engineering and developer support for Arm Mbed to consulting engineering for Pelion IoT. Jenny is co-author of the O’Reilly book "AI at the Edge: Solving Real World Problems with Embedded Machine Learning".
If you’re using artificial intelligence to build smart devices such as computer vision monitoring for predictive maintenance, you know that collecting, cleaning, and labeling data is time consuming, tedious, and expensive. Edge Impulse and pretrained models from NVIDIA TAO help get you on the fast track to enterprise-grade production with 100+ NVIDIA-optimized model architectures, like vision transformers and fully attentional networks. Fine-tune the models with your own data, enabling a much faster development process.
Watch the webinar, ‘Fast Track AI to the Edge with NVIDIA TAO & Edge Impulse’, to hear from NVIDIA’s Debraj Sinha (Product Marketing Manager), Edge Impulse’s Jan Jongboom (CTO & Cofounder) and Jenny Plunkett (Senior Engineer) about the latest in edge AI and how to leverage pretrained models from NVIDIA to deploy on edge devices.
Learn how to:
Debraj is a Product Marketing Manager for Metropolis at NVIDIA, focusing on building smarter spaces around the world with vision AI applications. Debraj collaborates with partners ranging from startups to Fortune 500 companies to market AI applications that drive safety and efficiency gains.
Jan Jongboom is an embedded engineer and machine learning advocate, always looking for ways to gather more intelligence from the real world. He has shipped devices, worked on the latest network tech, simulated microcontrollers and there's a monument in San Francisco with his name on it. Currently he serves as the cofounder and CTO of Edge Impulse, the leading development platform for embedded machine learning with 140,000+ projects.
Jenny Plunkett is a software engineer, technical speaker and content creator, working as a Senior Developer Relations Engineer at Edge Impulse. Since graduating from The University of Texas she has been working in the IoT space, from customer engineering and developer support for Arm Mbed to consulting engineering for Pelion IoT. Jenny is co-author of the O’Reilly book "AI at the Edge: Solving Real World Problems with Embedded Machine Learning".