TinyML for Smart Agriculture Training Course

This expert-led session in South Africa empowers advanced professionals with the capability to design, optimise, and deploy complete TinyML pipelines. Learners will acquire the skills to gather data, train low-energy models, and test practical applications through hands-on labs.

This instructor-led, live training in South Africa (online or onsite) is aimed at intermediate-level embedded systems engineers and AI developers who wish 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 live, instructor-led training in South Africa empowers advanced practitioners to optimise TinyML models for embedded devices with limited resources. Learners will apply quantisation and pruning, build low-latency inference pipelines, and measure performance against memory and energy constraints.

This instructor-led training in South Africa enables 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 handling in constrained environments.

This instructor-led, live training in South Africa (online or onsite) is aimed at 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.
• Optimise 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 South Africa teaches advanced professionals how to integrate TinyML into autonomous robotics. Participants will design optimised models, implement on-device perception pipelines, and deploy lightweight AI on embedded hardware for real-time autonomy.

This instructor-led, live training in South Africa (online or onsite) is designed for intermediate-level embedded engineers, IoT developers, and AI researchers keen on implementing TinyML techniques for AI-driven applications on energy-efficient hardware.

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

• Grasping the fundamentals of TinyML and edge AI.
• Deploying lightweight AI models onto microcontrollers.
• Optimising AI inference for minimal power consumption.
• Integrating TinyML with practical IoT applications.

This instructor-led live training in South Africa focuses on deploying TinyML solutions for healthcare monitoring and diagnostics. Learners will acquire the skills to design models for real-time health data, optimise them for low-power wearables, and guarantee clinical reliability. The course includes practical lab exercises.

This instructor-led, live training in South Africa (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 live, instructor-led training in South Africa assists beginners and intermediate-level learners in constructing practical TinyML applications using Raspberry Pi and Arduino. The curriculum encompasses data acquisition, model refinement, and edge deployment, ultimately guiding participants to engineer efficient, real-world embedded AI prototypes.