TinyML Training in Durban

This instructor-led, live training in Durban (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 expert-led session in Durban 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 training in Durban 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 Durban 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 21-hour instructor-led course on Durban empowers intermediate professionals to implement TinyML in smart agriculture. You will learn to create lightweight models, integrate edge AI with IoT, and optimize systems for precision irrigation and pest detection through practical lab-based activities.

This instructor-led live training in Durban 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 live, instructor-led training in Durban 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, live training in Durban (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 instructor-led, live training in Durban (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 instructor-led, live training in Durban (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.