Online or onsite, instructor-led live TinyML training courses demonstrate through interactive hands-on practice how to use machine learning on ultra-low-power devices to enable AI-driven applications in resource-constrained environments.
TinyML training is available as "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Onsite live TinyML training can be carried out locally on customer premises in Brasilia or in NobleProg corporate training centers in Brasilia.
NobleProg -- Your Local Training Provider
Brasilia - Corporate Financial Center
SCN, Qd2 - Block A - Cj. 503/504 , Brasília, Brazil
The Brasilia Corporate Financial Center is situated in a striking office complex in the Northern Commercial Sector, one of the main business districts. It is close to the Monumental Axis road which divides the city. The Brasilia Corporate Financial Center has an auditorium and cyber café and the complex offers restaurant and gym facilities, a helipad and parking.
Brasilia - Parque Cidade Corporate Building
Edifício Parque Cidade Corporate Brasilia DF , Brasilia, Brazil, 70308-200
Elevate the pace of your business operations at our workspace located in the thriving Parque Cidade Corporate Building in Brasília. Adorned with a striking glass façade, this triple-A-rated high-rise not only offers an impressive exterior but also provides unparalleled networking and growth prospects right at your fingertips.
Welcome your clients to a bright and conducive environment, boasting breathtaking views of Brasília from the rooftop terrace. When it’s time to recharge, unwind amidst the tranquil ambiance of Parque da Cidade, just a short stroll away. And for entertaining your guests, indulge in a variety of bars and restaurants conveniently situated nearby.
This instructor-led, live training in Brasilia (online or onsite) is aimed at intermediate-level embedded engineers, IoT developers, and AI researchers who wish to implement TinyML techniques for AI-powered applications on energy-efficient hardware.
By the end of this training, participants will be able to:
Understand the fundamentals of TinyML and edge AI.
Deploy lightweight AI models on microcontrollers.
Optimize AI inference for low-power consumption.
Integrate TinyML with real-world IoT applications.
This instructor-led, live training in Brasilia (online or onsite) is aimed at intermediate-level IoT developers, embedded engineers, and AI practitioners who wish to implement TinyML for predictive maintenance, anomaly detection, and smart sensor applications.
By the end of this training, participants will be able to:
Understand the fundamentals of TinyML and its applications in IoT.
Set up a TinyML development environment for IoT projects.
Develop and deploy ML models on low-power microcontrollers.
Implement predictive maintenance and anomaly detection using TinyML.
Optimize TinyML models for efficient power and memory usage.
This instructor-led, live training in Brasilia (online or onsite) is aimed at intermediate-level embedded systems engineers and AI developers who wish to deploy machine learning models on microcontrollers using TensorFlow Lite and Edge Impulse.
By the end of this training, participants will be able to:
Understand the fundamentals of TinyML and its benefits for edge AI applications.
Set up a development environment for TinyML projects.
Train, optimize, and deploy AI models on low-power microcontrollers.
Use TensorFlow Lite and Edge Impulse to implement real-world TinyML applications.
Optimize AI models for power efficiency and memory constraints.
This instructor-led, live training in Brasilia (online or onsite) is aimed at beginner-level engineers and data scientists who wish to understand TinyML fundamentals, explore its applications, and deploy AI models on microcontrollers.
By the end of this training, participants will be able to:
Understand the fundamentals of TinyML and its significance.
Deploy lightweight AI models on microcontrollers and edge devices.
Optimize and fine-tune machine learning models for low-power consumption.
Apply TinyML for real-world applications such as gesture recognition, anomaly detection, and audio processing.
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