TinyML with Raspberry Pi and Arduino Training Course

This instructor-led training in Spain empowers advanced professionals with the expertise to architect, optimize, and deploy complete TinyML workflows. Learners will gain skills in data acquisition, training low-power models, and testing real-world applications through hands-on laboratories.

Tailored for intermediate embedded systems engineers and AI developers, this instructor-led, live training in Spain (online or onsite) guides you through deploying machine learning models on microcontrollers using TensorFlow Lite and Edge Impulse.

By the conclusion of this training, you will have the capability to:

• Comprehend the essentials of TinyML and its value in edge AI contexts.
• Establish a comprehensive development workspace for TinyML tasks.
• Develop, optimize, and deploy AI models on low-energy microcontrollers.
• Utilize TensorFlow Lite and Edge Impulse to create real-world TinyML solutions.
• Fine-tune AI models to meet power efficiency and memory restriction requirements.

This instructor-led, live training in Spain equips 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 Spain empowers advanced professionals to secure TinyML pipelines on edge devices. It covers the implementation of privacy-preserving techniques, hardening models against adversarial threats, and applying best practices for secure data handling in constrained environments.

This live, instructor-led training, conducted in Spain (either online or onsite), is tailored for beginner-level engineers and data scientists seeking to master the basics of TinyML, investigate its diverse applications, and implement AI models on microcontrollers.

By the conclusion of this course, participants will have the capability to:

• Comprehend the core concepts of TinyML and appreciate its importance.
• Deploy efficient, lightweight AI models on microcontrollers and edge devices.
• Adjust and optimize machine learning models for energy-efficient operation.
• Utilize TinyML in real-world contexts such as gesture recognition, anomaly detection, and audio processing.

This live, instructor-led training in Spain guides advanced professionals in integrating TinyML into autonomous robotics. Learners will design optimized models, implement on-device perception pipelines, and deploy lightweight AI on embedded hardware to achieve real-time autonomy.

This instructor-led live training, available Spain (online or onsite), is tailored for intermediate-level embedded engineers, IoT developers, and AI researchers looking to apply TinyML techniques for AI-driven applications on energy-efficient hardware.

By the end of the program, participants will be able to:

• Comprehend the basics of TinyML and edge AI.
• Deploy lightweight AI models on microcontrollers.
• Optimize AI inference for reduced power usage.
• Integrate TinyML into real-world IoT scenarios.

This live, instructor-led training in Spain focuses on the implementation of TinyML solutions for healthcare monitoring and diagnostics. Participants will learn to design models for real-time health data, optimise them for low-power wearables, and ensure clinical reliability. The course includes hands-on laboratory exercises.

This instructor-led, live training in Spain (either online or onsite) is tailored for intermediate-level IoT developers, embedded engineers, and AI professionals looking to implement TinyML for predictive maintenance, anomaly detection, and smart sensor applications.

By the end of this program, participants will have the ability to:

• Comprehend the fundamentals of TinyML and its application in IoT contexts.
• Establish a robust TinyML development environment for IoT projects.
• Create and deploy ML models on low-power microcontrollers.
• Implement predictive maintenance and anomaly detection workflows using TinyML.
• Optimize TinyML models for superior power and memory performance.

This 21-hour instructor-led course in Spain 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.