TinyML for Smart Agriculture Training Course

This instructor-led course at Kenya empowers advanced professionals to design, optimize, and implement complete TinyML pipelines. Through practical labs, learners will acquire the ability to gather data, train energy-efficient models, and validate real-world applications.

This instructor-led, live training conducted in Kenya (either 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 conclusion 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 Kenya empowers advanced practitioners to fine-tune TinyML models for resource-limited embedded devices. Participants will apply quantization and pruning, develop low-latency inference pipelines, and measure performance against memory and energy boundaries.

This instructor-led course in Kenya empowers advanced professionals to safeguard TinyML pipelines on edge devices. Participants will acquire the skills to deploy privacy-preserving techniques, reinforce models against adversarial threats, and apply optimal practices for secure data processing in resource-limited settings.

This guided, live training in Kenya (online or in-person) is designed for novice engineers and data scientists who want to grasp the core concepts of TinyML, discover its practical uses, and deploy artificial intelligence models onto microcontrollers.

Upon completing this training, participants will be capable of:

• Grasping the fundamental principles of TinyML and its importance.
• Deploying efficient AI models on microcontrollers and edge hardware.
• Optimizing and refining machine learning models to minimize power usage.
• Utilizing TinyML for practical scenarios such as gesture recognition, anomaly detection, and audio processing.

This instructor-led, live training in Kenya empowers advanced professionals to integrate 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 Kenya (delivered online or on-site) targets intermediate-level embedded engineers, IoT developers, and AI researchers interested in implementing TinyML techniques for AI-powered applications on energy-efficient hardware.

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

• Comprehend the foundational aspects of TinyML and edge AI.

• Deploy lightweight AI models on microcontrollers.
• Optimize AI inference for minimal power usage.
• Integrate TinyML with real-world IoT applications.

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

This instructor-led, live training in Kenya (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.