TinyML for Smart Agriculture Training Course

This instructor-led training in Sweden provides advanced professionals with the expertise to design, optimize, and deploy complete TinyML pipelines. Participants will gain the ability to collect data, train low-power models, and test real-world applications through practical labs.

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

Upon completing this training, participants will be able to:

• Grasp the fundamentals 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 limitations.

This live, instructor-led training in Sweden prepares advanced practitioners to optimize TinyML models for resource-constrained embedded devices. Participants will apply quantization and pruning, build low-latency inference pipelines, and evaluate performance against memory and energy constraints.

This instructor-led training in Sweden empowers advanced professionals to secure TinyML pipelines on edge devices. Participants will learn to implement privacy-preserving techniques, strengthen models against adversarial threats, and apply best practices for secure data handling in constrained environments.

This live, instructor-led training located in Sweden (available online or on-site) is designed for beginner-level engineers and data scientists who want to learn the basics of TinyML, explore its applications, and deploy AI models on microcontrollers.

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

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

• Grasping the core principles of TinyML and edge AI.
• Deploying lightweight AI models onto microcontrollers.
• Optimizing AI inference to minimize power consumption.
• Integrating TinyML with practical IoT applications.

This live, instructor-led training on Sweden focuses on deploying TinyML for healthcare monitoring and diagnostics. Learners will acquire skills in designing real-time health data models, optimizing them for low-power wearables, and guaranteeing clinical reliability. The course includes practical lab sessions.

This instructor-led, live training in Sweden (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 core principles of TinyML and its role in IoT ecosystems.
• Establish a TinyML development environment tailored for IoT projects.
• Create and deploy ML models on low-power microcontrollers.
• Apply TinyML to achieve predictive maintenance and anomaly detection.
• Optimize TinyML models to ensure efficient power and memory utilization.