TinyML with Raspberry Pi and Arduino Training Course

This instructor-facilitated course in Botswana empowers advanced professionals to architect, optimize, and deploy holistic TinyML pipelines. Learners will develop the ability to gather data, train low-power models, and evaluate practical applications through hands-on laboratory sessions.

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

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

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

Upon completing this training, participants will be capable of:

• Grasping the core principles of TinyML and its importance.
• Deploying streamlined AI models on microcontrollers and edge devices.
• Optimising and fine-tuning machine learning models for low-power usage.
• Applying TinyML in real-world scenarios such as gesture recognition, anomaly detection, and audio processing.

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

This instructor-led live training in Botswana (online or at your premises) targets intermediate-level embedded engineers, IoT developers, and AI researchers looking to apply TinyML techniques for AI-driven applications on energy-efficient hardware.

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

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

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

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