TinyML for Smart Agriculture Training Course

This live, instructor-led program in Norway empowers advanced professionals to design, optimize, and deploy complete TinyML pipelines. Learners will acquire skills in data acquisition, low-power model training, and real-world application testing through hands-on labs.

This instructor-led, live training held in Norway (online or onsite) targets intermediate-level embedded systems engineers and AI developers who aim to deploy machine learning models on microcontrollers utilizing TensorFlow Lite and Edge Impulse.

By the conclusion of this training, participants will be able to:

• Grasp the core principles of TinyML and its advantages for edge AI solutions.
• Establish a development environment suitable for TinyML initiatives.
• Training, refining, and deploying AI models on power-efficient microcontrollers.
• Utilizing TensorFlow Lite and Edge Impulse to build practical TinyML applications.
• Enhancing AI models for improved power efficiency and to navigate memory limitations.

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

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

This instructor-led, live training in Norway (online or onsite) is designed for beginner-level engineers and data scientists who want to grasp the core principles of TinyML, investigate its practical uses, and deploy AI models onto microcontrollers.

Upon completing this training, participants will be able to:

• Comprehend the basics of TinyML and its importance.
• Deploy lightweight AI models on microcontrollers and edge devices.
• Optimize and fine-tune machine learning models to minimize power usage.
• Implement TinyML in real-world scenarios such as gesture recognition, anomaly detection, and audio processing.

This instructor-led, live training in Norway guides advanced professionals on integrating TinyML into autonomous robotics. Learners will design optimized models, build on-device perception pipelines, and deploy lightweight AI on embedded hardware to achieve real-time autonomy.

This instructor-led, live training in Norway (online or onsite) is designed for intermediate-level embedded engineers, IoT developers, and AI researchers who want 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 course in Norway focuses on deploying TinyML solutions for health monitoring and diagnostics. Learners will acquire the skills to design models for real-time physiological data, optimize them for low-power wearables, and guarantee clinical dependability. The curriculum includes practical lab exercises.

This instructor-led, live training in Norway (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.