TinyML Training in Gaia

This instructor-led, live training in Gaia (online or onsite) is designed for intermediate-level embedded engineers, IoT developers, and AI researchers who aim to implement TinyML techniques for AI-powered applications on energy-efficient hardware.

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

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

This instructor-led course in Gaia empowers advanced professionals to design, optimise, and deploy full-scale TinyML pipelines. Attendees will gain the ability to gather data, train low-energy models, and validate real-world applications through hands-on lab sessions.

This instructor-led training in Gaia empowers advanced professionals to secure TinyML pipelines on edge devices. Learners will acquire the skills to implement privacy-preserving techniques, harden models against adversarial threats, and apply best practices for secure data handling in constrained environments.

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

This 21-hour instructor-led course in Gaia equips intermediate professionals to implement TinyML for smart agriculture. Gain skills in building lightweight models, integrating edge AI with IoT, and optimizing systems for precision irrigation and pest detection through hands-on lab activities.

This live, instructor-led training in Gaia focuses on the implementation of TinyML solutions for health monitoring and diagnostics. Learners will master the design of models for real-time physiological data, optimization strategies for low-power wearables, and methods to guarantee clinical reliability. The curriculum includes practical laboratory exercises.

This instructor-led, live training in Gaia prepares advanced practitioners to optimise TinyML models for resource-constrained embedded devices. Learners will apply quantization and pruning, develop low-latency inference pipelines, and benchmark performance against memory and energy constraints.

This instructor-led, live training in Gaia (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:

• Grasp the fundamentals of TinyML and its applications in IoT.
• Configure a TinyML development environment for IoT projects.
• Create 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 instructor-led, live training in Gaia (online or onsite) targets intermediate-level embedded systems engineers and AI developers who aim to deploy machine learning models on microcontrollers using TensorFlow Lite and Edge Impulse.

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

• Understand the fundamentals of TinyML and its benefits for edge AI applications.
• Set up a development environment for TinyML projects.
• Train, optimize, and deploy AI models on low-power microcontrollers.
• Use TensorFlow Lite and Edge Impulse to implement real-world TinyML applications.
• Optimize AI models for power efficiency and memory constraints.

This instructor-led, live training in Gaia (online or onsite) is designed for beginner-level engineers and data scientists who wish to understand TinyML fundamentals, explore its applications, and deploy AI models on microcontrollers.

By the end of 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.