What you'll learn
Learn the benefits of edge AI and how to achieve value in less time while lowering operational costs
Build a team that leverages edge AI expertise to solve real-world problems
Understand which use cases are best solved with edge AI
Learn how to optimize ML at the edge and how it complements cloud ML
Design and operationalize data for edge AI applications
Learn an iterative workflow for developing AI systems
About the authors
Head of Machine Learning, Edge Impulse
Daniel Situnayake is Head of Machine Learning at Edge Impulse, where he leads embedded machine learning R&D. He's coauthor of the book AI at the Edge: Solving Real-World Problems with Embedded Machine Learning, along with TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers, the standard textbook on embedded machine learning, and has delivered guest lectures at Harvard, UC Berkeley, and UNIFEI.
Dan previously worked on TensorFlow Lite at Google, and co-founded Tiny Farms, the first US company using automation to produce insect protein at industrial scale. He began his career lecturing in automatic identification and data capture at Birmingham City University.
Senior Developer Relations Engineer, Edge Impulse
Jenny Plunkett is a Senior Developer Relations Engineer at Edge Impulse, where she is a technical speaker, developer evangelist, and technical content creator. In addition to maintaining the Edge Impulse documentation, she has also created developer-facing resources for Arm Mbed OS and Pelion IoT. She has presented workshops and tech talks for major tech conferences such as the Grace Hopper Celebration, Edge AI Summit, Embedded Vision Summit, and more. Jenny previously worked as a software engineer and IoT consultant for Arm Mbed and Pelion. She graduated with a B.S. in Electrical Engineering from The University of Texas at Austin.