TinyML for Smart Agriculture Training Course

This instructor-led training on the Netherlands empowers advanced professionals with the expertise to design, optimize, and deploy complete TinyML pipelines. Participants will master data collection, low-power model training, and real-world application testing through practical labs.

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

Upon completing this training, participants will be capable of:

• Grasping the core principles of TinyML and its advantages for edge AI applications.
• Configuring a development environment suitable for TinyML projects.
• Training, optimizing, and deploying AI models on low-power microcontrollers.
• Leveraging TensorFlow Lite and Edge Impulse to build practical TinyML solutions.
• Enhancing AI models for improved power efficiency and adherence to memory limitations.

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

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

This guided, live training in the Netherlands (online or in person) is designed for engineers and data scientists at the beginner level who want to grasp the core principles of TinyML, investigate its practical uses, and deploy AI models on microcontrollers.

Upon completion of this training, participants will be capable of:

• Grasping the core concepts of TinyML and its importance.
• Deploying compact AI models on microcontrollers and edge devices.
• Optimizing and refining machine learning models to minimize power usage.
• Utilizing TinyML for real-world scenarios such as gesture recognition, anomaly detection, and audio processing.

This live, instructor-led training in the Netherlands 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 in the Netherlands (online or onsite) is designed for intermediate-level embedded engineers, IoT developers, and AI researchers who wish to apply TinyML techniques for AI-enabled applications on energy-efficient hardware.

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

• Grasp the core concepts of TinyML and edge AI.
• Deploy lightweight AI models onto microcontrollers.
• Optimize AI inference to minimize power consumption.
• Integrate TinyML solutions into practical IoT applications.

This live, instructor-led training in the Netherlands focuses on the implementation of 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 laboratory exercises.

This instructor-led, live training in the Netherlands (online or onsite) is designed for 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.