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 San José or in NobleProg corporate training centers in San José.
NobleProg -- Your Local Training Provider
San Jose - Plaza Roble Las Terrazas
Centro Corporativo Plaza Roble, San José, Costa rica
The San Jose Plaza Roble Las Terrazas Center is situated in one of six buildings connected by covered walkways that make up the secure Plaza Roble complex. With direct access to a major highway intersection, it is easy to get to downtown San Jose, 7km away, and a 15-minute drive to the international airport.
San Jose - Boulevard Dent
6184 Boulevard Dent San Jose San Pedro , San Jose, Costa rica, 11501
Create a fresh headquarters for your company in Costa Rica, featuring fully equipped office facilities at 6184 Boulevard Dent. Dive right into operations in the rapidly expanding capital, situated within the vibrant business hub of The Alameda. Benefit from convenient access to public transportation via bus and train for commuting, as well as the attractions of Downtown San Jose.
This instructor-led, live training in San José (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 San José (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 San José (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 San José (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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