TinyML for Smart Agriculture Training Course

This instructor-led session on Serbia empowers senior professionals to architect, optimize, and release full-scale TinyML pipelines. Through practical labs, learners will acquire the ability to gather data, train energy-efficient models, and validate real-world applications.

This instructor-led, live training in Serbia (online or on-site) is designed for 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 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 real-world TinyML solutions.
• Optimize AI models to meet power efficiency and memory limitations.

This instructor-led live training in Serbia enables advanced practitioners to optimize TinyML models for resource-constrained embedded devices. Participants will implement quantization and pruning techniques, construct low-latency inference pipelines, and benchmark performance against memory and energy constraints.

This instructor-led course in Serbia enables advanced professionals to fortify TinyML pipelines on edge devices. It covers the implementation of privacy-preserving strategies, hardening models against adversarial risks, and adhering to best practices for secure data management in constrained environments.

This guided, live training in Serbia (available online or in-person) is designed for beginner-level engineers and data scientists who want to understand the fundamentals of TinyML, explore its applications, and deploy AI models on microcontrollers.

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

• Grasping the core principles of TinyML and its importance.
• Implementing lightweight AI models on microcontrollers and edge devices.
• Optimizing and fine-tuning machine learning models to minimize power usage.
• Utilizing TinyML for practical scenarios including gesture recognition, anomaly detection, and audio processing.

This live, instructor-led training in Serbia 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 Serbia (online or onsite) is designed for intermediate-level embedded engineers, IoT developers, and AI researchers looking to implement TinyML techniques for AI-driven 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 on microcontrollers.
• Optimize AI inference processes for minimal power consumption.
• Integrate TinyML solutions into practical IoT applications.

This instructor-led live training in Serbia focuses on the practical implementation of TinyML for healthcare monitoring and diagnostics. Learners will master the design of models for real-time health data, optimization strategies for low-power wearables, and methods to guarantee clinical reliability. The curriculum includes hands-on lab exercises to reinforce theoretical concepts.

This instructor-led, live training in Serbia (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.