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 trainings in Abu Dhabi can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Park Rotana Complex
2, Abu Dhabi, United Arab Emirates
Situated within the Park Complex and adjacent to TwoFour54 and Khalifa Park. Park Rotana is 15 minutes drive from Abu Dhabi International Airport and Yas Island.
Driving directions from Abu Dhabi International Airport:
Drive towards Abu Dhabi by highway No. E 10, Keep driving straight after passing the “Al Raha mall”. Follow the signage “Sheikh Zayed Bridge” to drive through the bridge, Keep in the right lane when you finish the bridge. You are now driving on “Salam / Eastern” road. Take the exit “Khalifa Park & Ministries Complex” from extreme right lane to make a “U turn” over the underpass, Follow the signal “Park Rotana Complex” and take right to the roundabout. After it, turn left in the roundabout and drive straight until you reach Park Rotana.
Radisson Blu Hotel & Resort, Abu Dhabi Corniche
2, Abu Dhabi, United Arab Emirates
Our location provides a memorable setting for both business and social events near local attractions and beaches on the Arabian Gulf. Rely on Radisson Blu to deliver a flawless meeting experience incorporating elements like audiovisual equipment and customized menus, as well as free Wi-Fi. Our stunning ballroom (with adjoining terrace) is divisible into three separate areas with private entrances for a truly impressive events space
This instructor-led, live training in Abu Dhabi (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 Abu Dhabi (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 Abu Dhabi (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 Abu Dhabi (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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