TinyAI & TinyML for IoT –
A revolution for the Consumer Goods Industry

Free webinar:

TinyAI & TinyML will revolutionize IoT and the consumer goods industry. Remember the early days of amazon? No one believed in the success of e-commerce. Artificial Intelligence, Machine Learning and Deep Learning will drive and change developments in all industries in the future. Get the newest insight into TinyML and TinyAI for IoT

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TinyML is a rapidly-growing area of machine learning that targets Artificial Intelligence implementations for use on low-powered devices with scarce computer and memory resources — like the millions of sensors deployed in Internet of Things (IoT). This is achieved at exceptionally low power (mW range and below!) using small, essentially self-contained Neural Networks.
Upgrading the potentially billions of existing microcontrollers (MCUs) today with TinyML capabilities allows to bring machine intelligence right next to the physical world and unlock amazing applications by leveraging the huge amounts of data generated from on-device sensors – audio, vision, Instrument Measurement Unit, biomedical.

This is particularly relevant in the Consumer goods industry, especially with the proliferation of smart appliances, connected homes and accelerated digitization.
This webinar will enable attendants to learn about the wonderful world of TinyML, as well as the latest trends in building industrial-grade TinyML applications. Attendants will be introduced to how to build end-to-end TinyML applications using the latest best practices in embedded machine learning and will get an overview about the whole workflow of a TinyML system, from data collection, to building models, optimizing them for embedded use, and deploying them in the real world.

Webinar Agenda

  • Definition of TinyAI & TinyML and their challenges

  • Key Use Cases of TinyML and applications

  • TinyML as a business opportunity

Watch out for our next webinar:
Trends in Consumer IoT and Smart Home


Alexander Nyßen

Dr. Alexander Nyßen
Dr. Alexander Nyßen leads the competence center IoT Solutions at itemis, in which all services and staff dedicated to the engineering of holistic IoT solutions are comprised. Having graduated from RWTH Aachen University as a computer scientist with a specialization in software engineering, he concluded his doctoral studies at the same alma mater with a focus on model-based development of small embedded systems in the Industrial Automation domain. Joining itemis in 2009, his professional focus has since been on developing model-based methodologies and on engineering complex mechatronical and cyber-physical systems, predominantly in the Automotive domain. As a coach and toolsmith, he has helped various customers to define their engineering methods and build-up their related toolchains. As a system engineer he has furthermore supported the engineering of most different systems. He is actively pursuing both, even today.

LinkedIn: https://www.linkedin.com/in/nyssen/ 
Website: https://www.itemis.com

Hajar Mousannif

Prof. Dr. Hajar Mousannif
Prof. Dr. Hajar Mousannif is an associate professor and founder of a Bachelor and a Master program in Artificial Intelligence and Data Science at Cadi Ayyad University, Morocco. She holds a PhD degree in computer Science, a Habilitation degree in Artificial Intelligence and an Engineering degree in Telecommunications. Her primary research interests include Artificial Intelligence, Machine Learning, TinyML and IoT. In addition to her academic experience, she chaired the Program Committee of many international conferences. She leads both the TinyML and the Nvidia AI Moroccan chapters. Prof. Dr. Hajar Mousannif holds two patents on her work on Artificial Intelligence and was selected among 5 best female researchers in North Africa. She received many international awards, such as the L'Oréal-UNESCO Award and the Emerald Litterati Prize for Excellence. In December 2020, she was selected as the GOLD winner of the prestigious International prize: "WomenTech Global AI Inclusion Award", among more than 2300 participants.

LinkedIn: https://www.linkedin.com/in/hajar-mousannif 
Website: https://mousannifhajar.com 

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Webinar recording including the presentation.