TinyML for Smart Agriculture Training Course

TinyML refers to the deployment of highly optimised machine learning models onto edge devices with limited resources.

This guided, live training session (available online or in-person) is designed for advanced technical professionals who wish to create, optimise, and deploy full-scale TinyML pipelines.

Upon completing this training, participants will be able to:

• Gather, prepare, and manage datasets suitable for TinyML applications.
• Train and optimise models for low-power microcontrollers.
• Convert models into lightweight formats ideal for edge devices.
• Deploy, test, and monitor TinyML applications on actual hardware.

Course Format

• Instructor-led lectures and technical discussions.
• Practical labs and iterative experimentation.
• Hands-on deployment on microcontroller-based platforms.

Customization Options

• To tailor the training to specific toolchains, hardware boards, or internal workflows, please contact us to make arrangements.

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.

TinyML involves deploying machine learning models on hardware with strict resource limitations.

This instructor-led live training, available online or onsite, is designed for advanced practitioners looking to optimize TinyML models for low-latency, memory-efficient deployment on embedded devices.

After completing this training, participants will be able to:

• Utilise quantization, pruning, and compression techniques to reduce model size without compromising accuracy.
• Benchmark TinyML models for latency, memory usage, and energy efficiency.
• Implement optimized inference pipelines on microcontrollers and edge devices.
• Evaluate the trade-offs between performance, accuracy, and hardware constraints.

Course Format

• Instructor-led presentations supported by technical demonstrations.
• Practical optimization exercises and comparative performance testing.
• Hands-on implementation of TinyML pipelines in a controlled lab environment.

Course Customization Options

• For tailored training aligned with specific hardware platforms or internal workflows, please contact us to customize the program.

TinyML refers to the practice of deploying machine learning models on low-power, resource-constrained devices that operate at the network edge.

This instructor-led live training, available online or onsite, targets advanced-level professionals keen on securing TinyML pipelines and implementing privacy-preserving techniques within edge AI applications.

Upon completing this course, participants will be equipped to:

• Identify security risks specific to on-device TinyML inference.
• Deploy privacy-preserving mechanisms for edge AI implementations.
• Strengthen TinyML models and embedded systems against adversarial threats.
• Apply best practices for secure data handling in constrained environments.

Course Format

• Engaging lectures complemented by expert-led discussions.
• Practical exercises focused on real-world threat scenarios.
• Hands-on implementation using embedded security and TinyML tooling.

Course Customization Options

• Organizations may request a tailored version of this training to align with their specific security and compliance needs.

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.

TinyML provides a framework for deploying machine learning models onto low-power microcontrollers and embedded platforms utilised in robotics and autonomous systems.

This instructor-led, live training (available online or onsite) is designed for advanced-level professionals seeking to integrate TinyML-based perception and decision-making capabilities into autonomous robots, drones, and intelligent control systems.

Upon completing this course, participants will be able to:

• Design optimised TinyML models for robotics applications.
• Implement on-device perception pipelines for real-time autonomy.
• Integrate TinyML into existing robotic control frameworks.
• Deploy and test lightweight AI models on embedded hardware platforms.

Format of the Course

• Technical lectures combined with interactive discussions.
• Hands-on labs focusing on embedded robotics tasks.
• Practical exercises simulating real-world autonomous workflows.

Course Customization Options

• For organisation-specific robotics environments, customization can be arranged upon request.

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.

TinyML involves embedding machine learning capabilities into low-power, resource-constrained wearable and medical devices.

This instructor-led live training, available online or onsite, is designed for intermediate-level practitioners aiming to implement TinyML solutions for healthcare monitoring and diagnostic purposes.

Upon completing this training, participants will be able to:

• Design and deploy TinyML models for real-time health data processing.
• Collect, preprocess, and interpret biosensor data to derive AI-driven insights.
• Optimize models to suit low-power and memory-limited wearable devices.
• Evaluate the clinical relevance, reliability, and safety of TinyML-generated outputs.

Format of the Course

• Lectures accompanied by live demonstrations and interactive discussions.
• Hands-on practice using wearable device data and TinyML frameworks.
• Implementation exercises conducted in a guided lab environment.

Course Customization Options

• For tailored training that aligns with specific healthcare devices or regulatory workflows, please contact us to customize the program.

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.

TinyML refers to a machine learning methodology designed for devices with limited resources and small form factors.

This instructor-led live training, available online or on-site, is tailored for learners at beginner to intermediate levels who aim to develop functional TinyML applications using Raspberry Pi, Arduino, and comparable microcontrollers.

Upon completing this training, participants will acquire the ability to:

• Gather and prepare data specifically for TinyML initiatives.
• Train and fine-tune compact machine learning models for microcontroller environments.
• Deploy TinyML models on Raspberry Pi, Arduino, and other similar boards.
• Create complete embedded AI prototypes from start to finish.

Course Format

• Instructor-led presentations combined with guided discussions.
• Practical exercises and hands-on experimentation.
• Live-lab projects utilising real hardware.

Customisation Options

• For bespoke training aligned with your specific hardware requirements or use cases, please contact us to make arrangements.