TinyML with Raspberry Pi and Arduino Training Course

This instructor-led program in Slovakia empowers senior professionals to design, refine, and execute holistic TinyML workflows. Attendees will master data acquisition, low-power model training, and real-world application testing through hands-on laboratory exercises.

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

Upon completing this training, participants will be able to:

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

This live, instructor-led training in Slovakia prepares advanced practitioners to optimize TinyML models for embedded devices with limited resources. Learners will apply quantization and pruning techniques, build low-latency inference pipelines, and evaluate performance against memory and energy constraints.

This instructor-led course in Slovakia empowers advanced professionals to protect TinyML pipelines on edge devices. You will gain the skills to implement privacy-preserving techniques, fortify models against adversarial threats, and apply secure data handling best practices in constrained environments.

This instructor-led, live training in Slovakia (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 Slovakia guides advanced professionals in integrating TinyML into autonomous robotics. Participants will learn to 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 session Slovakia (online or onsite) is tailored for intermediate-level embedded engineers, IoT developers, and AI researchers aiming 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 Slovakia focuses on implementing TinyML solutions for healthcare monitoring and diagnostics. Participants will learn to design models for real-time health data processing, optimize them for low-power wearables, and ensure clinical reliability. The course includes hands-on lab exercises.

This instructor-led, live training in Slovakia (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 Slovakia equips intermediate professionals to implement TinyML in smart agriculture. Discover how to create lightweight models, integrate edge AI with IoT, and optimize systems for precision irrigation and pest detection through hands-on laboratory practice.