TinyML for Smart Agriculture Training Course

TinyML involves the deployment of optimized machine learning models onto edge devices with limited resources.

This guided, live training session (available online or at your location) is designed for advanced technical professionals aiming to create, optimize, and implement comprehensive TinyML pipelines.

Upon completing this course, participants will acquire the skills to:

• Gather, prepare, and oversee datasets tailored for TinyML applications.
• Train and fine-tune models specifically for low-power microcontrollers.
• Transform models into lightweight formats compatible with edge devices.
• Deploy, evaluate, and oversee TinyML applications in actual hardware setups.

Course Structure

• 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 processes, please reach out to us to make arrangements.

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.

TinyML involves the deployment of machine learning models onto hardware with severe resource limitations.

This guided, live training session (available online or on-site) targets advanced practitioners seeking to optimize TinyML models for low-latency and memory-efficient deployment on embedded devices.

Upon completion of this course, participants will be equipped to:

• Utilize quantization, pruning, and compression methods to decrease model size while maintaining accuracy.
• Benchmark TinyML models to assess latency, memory usage, and energy efficiency.
• Deploy optimized inference pipelines on microcontrollers and edge devices.
• Assess the trade-offs between performance, accuracy, and hardware constraints.

Course Format

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

Customization Options

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

TinyML involves deploying machine learning models onto low-power, resource-constrained devices at the network edge.

This instructor-led, live training (available online or onsite) is designed for advanced professionals seeking to secure TinyML pipelines and implement privacy-preserving techniques in edge AI applications.

Upon completing this course, participants will be able to:

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

Format of the Course

• Engaging lectures supported by expert-led discussions.
• Practical exercises emphasizing 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 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.

TinyML serves as a framework for deploying machine learning models on low-power microcontrollers and embedded platforms utilized in robotics and autonomous systems.

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

By the end of this course, participants will be equipped to:

• Create optimized TinyML models tailored for robotics applications.
• Build 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.

Course Format

• Technical lectures paired with interactive discussions.
• Practical labs focused on embedded robotics tasks.
• Hands-on exercises that simulate real-world autonomous workflows.

Customization Options

• Customization can be arranged upon request to suit organization-specific robotics environments.

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.

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

This instructor-led live training, available both online and onsite, targets intermediate-level practitioners seeking to implement TinyML solutions for healthcare monitoring and diagnostic purposes.

Upon completion of this training, participants will be able to:

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

Course Format

• Lectures complemented by live demonstrations and interactive discussions.
• Practical exercises involving wearable device data and TinyML frameworks.
• Guided lab exercises for model implementation.

Course Customization Options

• For customized training tailored to specific healthcare devices or regulatory workflows, please contact us to arrange the program.

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.

TinyML is a machine learning approach optimized for small, resource-constrained devices.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level learners who wish to build working TinyML applications using Raspberry Pi, Arduino, and similar microcontrollers.

Upon completing this training, attendees will gain the skills to:

• Collect and prepare data for TinyML projects.
• Train and optimize small machine learning models for microcontroller environments.
• Deploy TinyML models on Raspberry Pi, Arduino, and related boards.
• Develop end-to-end embedded AI prototypes.

Format of the Course

• Instructor-led presentations and guided discussions.
• Practical exercises and hands-on experimentation.
• Live-lab project work on real hardware.

Course Customization Options

• For tailored training aligned with your specific hardware or use case, please contact us to arrange.