TinyML with Raspberry Pi and Arduino Training Course

This instructor-led training in Italy empowers advanced professionals to design, optimize, and deploy full-scale TinyML pipelines. Through practical labs, learners will acquire data, train energy-efficient models, and validate real-world applications.

This instructor-led, live training in Italy (online or onsite) is designed for intermediate-level embedded systems engineers and AI developers who aim to deploy machine learning models on microcontrollers using TensorFlow Lite and Edge Impulse.

Upon completion of this training, participants will be able to:

• Grasp the core principles of TinyML and its advantages for edge AI applications.
• Configure a development environment suitable for TinyML projects.
• Train, optimize, and deploy AI models on low-power microcontrollers.
• Leverage TensorFlow Lite and Edge Impulse to build practical TinyML solutions.
• Enhance AI models for better power efficiency and to meet memory limitations.

This live, instructor-led session in Italy prepares advanced professionals to fine-tune TinyML models for resource-limited embedded devices. Learners will apply quantization and pruning techniques, build low-latency inference pipelines, and measure performance against memory and energy constraints.

This instructor-led training in Italy empowers advanced professionals to secure TinyML pipelines on edge devices. You will develop the ability to implement privacy-preserving techniques, reinforce models against adversarial attacks, and apply best practices for secure data handling within constrained environments.

This instructor-led, live training in Italy (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.

This live, instructor-led training in Italy 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 in Italy (online or onsite) targets intermediate-level embedded engineers, IoT developers, and AI researchers who wish to implement TinyML techniques for AI-driven applications on energy-efficient hardware.

Upon completion of this training, participants will be able to:

• Comprehend the core principles of TinyML and edge AI.
• Deploy lightweight AI models onto microcontrollers.
• Optimize AI inference for minimal power consumption.
• Integrate TinyML with practical IoT applications.

This instructor-led live training in Italy addresses the implementation of TinyML for healthcare monitoring and diagnostics. Participants will acquire skills to design models for real-time health data, optimize for low-power wearables, and guarantee clinical reliability. The curriculum includes hands-on laboratory exercises.

This instructor-led, live training in Italy (online or onsite) targets 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:

• Grasp the fundamentals of TinyML and its role in IoT.
• Configure a TinyML development environment tailored for IoT projects.
• Create and deploy ML models on low-power microcontrollers.
• Apply TinyML for predictive maintenance and anomaly detection.
• Optimize TinyML models to balance power efficiency and memory usage.

This 21-hour instructor-led course in Italy enables intermediate professionals to implement TinyML for smart agriculture. Participants will learn to construct lightweight models, integrate edge AI with IoT systems, and optimize for precision irrigation and pest detection within a hands-on lab environment.