TinyML with Raspberry Pi and Arduino Training Course

This instructor-led training in Australia provides advanced professionals with the skills to design, optimise, and deploy complete TinyML pipelines. Participants will learn to collect data, train low-power models, and test real-world applications through practical labs.

This instructor-led, live training in Australia (online or onsite) is designed for intermediate-level embedded systems engineers and AI developers looking 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 suitable for TinyML projects.
• Train, optimise, and deploy AI models on low-power microcontrollers.
• Utilise TensorFlow Lite and Edge Impulse to build real-world TinyML applications.
• Optimise AI models for power efficiency and memory limitations.

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

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

This instructor-led, live training in Australia (online or in-person) is designed for beginner-level engineers and data scientists keen on grasping the core principles of TinyML, investigating its use cases, and deploying AI models onto microcontrollers.

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

• Comprehending the fundamental concepts of TinyML and its importance.
• Implementing lightweight AI models on microcontrollers and edge devices.
• Optimising and fine-tuning machine learning models to minimise power usage.
• Applying TinyML to practical scenarios such as gesture recognition, anomaly detection, and audio processing.

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

This instructor-led, live training in Australia (online or onsite) is aimed at intermediate-level embedded engineers, IoT developers, and AI researchers who wish 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.
• Optimise AI inference for low-power consumption.
• Integrate TinyML with real-world IoT applications.

This instructor-led live training in Australia focuses on implementing TinyML solutions for healthcare monitoring and diagnostics. Participants will learn to design models for real-time health data, optimise for low-power wearables, and ensure clinical reliability, including hands-on lab exercises.

This instructor-led, live training in Australia (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 Australia empowers intermediate professionals to deploy TinyML for smart agriculture. Participants will learn to build lightweight models, integrate edge AI with IoT, and optimise for precision irrigation and pest detection in a hands-on lab setting.