TinyML with Raspberry Pi and Arduino Training Course

This instructor-led session in Mexico empowers advanced professionals to design, refine, and deploy comprehensive TinyML solutions. Learners will master data collection, low-power model training, and real-world application testing through practical labs.

This instructor-led, live training in Mexico (online or onsite) targets intermediate-level embedded systems engineers and AI developers seeking to deploy machine learning models on microcontrollers using TensorFlow Lite and Edge Impulse.

By the conclusion of this training, participants will be able to:

• Understand the core principles of TinyML and its benefits for edge AI applications.
• Establish a development environment for TinyML initiatives.
• Train, optimize, and deploy AI models on low-power microcontrollers.
• Use TensorFlow Lite and Edge Impulse to create real-world TinyML applications.
• Optimize AI models for power efficiency and memory limitations.

This live, instructor-led training in Mexico prepares advanced practitioners to optimize TinyML models for resource-constrained embedded devices. Participants will apply quantization and pruning, implement low-latency inference pipelines, and benchmark performance against memory and energy limits.

This instructor-led training in Mexico empowers advanced professionals to secure TinyML pipelines on edge devices. You will learn to implement privacy-preserving techniques, harden models against adversarial threats, and apply best practices for secure data handling in constrained environments.

This instructor-led live training in Mexico (online or onsite) is designed for beginner-level engineers and data scientists interested in grasping TinyML basics, exploring its uses, and deploying AI models on microcontrollers.

Upon completing this training, participants will be able to:

• Comprehend the core principles of TinyML and why it matters.
• Deploy lightweight AI models on microcontrollers and edge devices.
• Optimize and fine-tune machine learning models to minimize power usage.
• Implement TinyML in practical scenarios like gesture recognition, anomaly detection, and audio processing.

This instructor-led, live training in Mexico teaches advanced professionals how to integrate TinyML into autonomous robotics. Participants will design optimized models, implement on-device perception pipelines, and deploy lightweight AI on embedded hardware for real-time autonomy.

This instructor-led, live training in Mexico (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 live, instructor-led training in Mexico focuses on implementing TinyML solutions for healthcare monitoring and diagnostics. Participants will learn to design models for real-time health data, optimize them for low-power wearables, and ensure clinical reliability. The course includes hands-on lab exercises.

This instructor-led, live training in Mexico (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 21-hour instructor-led course in Mexico empowers intermediate professionals to deploy TinyML for smart agriculture. Learn to build lightweight models, integrate edge AI with IoT, and optimize for precision irrigation and pest detection in a hands-on lab setting.