TinyML for IoT Applications 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 in Kenya (online or onsite) is aimed at intermediate-level IT professionals and business managers who wish to understand the potential of IoT and edge computing for enabling efficiency, real-time processing, and innovation in various industries.

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

• Understand the principles of IoT and edge computing and their role in digital transformation.
• Identify use cases for IoT and edge computing in manufacturing, logistics, and energy sectors.
• Differentiate between edge and cloud computing architectures and deployment scenarios.
• Implement edge computing solutions for predictive maintenance and real-time decision-making.

This instructor-led, live training in Kenya (online or onsite) is designed for intermediate-level developers, system architects, and industry professionals who wish to leverage Edge AI to enhance IoT applications with intelligent data processing and analytics capabilities.

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

• Understand the fundamentals of Edge AI and its application in IoT.
• Set up and configure Edge AI environments for IoT devices.
• Develop and deploy AI models on edge devices for IoT applications.
• Implement real-time data processing and decision-making in IoT systems.
• Integrate Edge AI with various IoT protocols and platforms.
• Address ethical considerations and best practices in Edge AI for IoT.

This instructor-led, live training in Kenya (online or onsite) is designed for product managers and developers who wish to use Edge Computing to decentralize data management for faster performance, leveraging smart devices located on the source network.

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

• Understand the basic concepts and advantages of Edge Computing.
• Identify the use cases and examples where Edge Computing can be applied.
• Design and build Edge Computing solutions for faster data processing and reduced operational costs.

This instructor-led, live training in Kenya (online or onsite) is designed for intermediate-level professionals who wish to apply Federated Learning to optimize IoT and edge computing solutions.

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

• Grasp the principles and advantages of Federated Learning in IoT and edge computing.
• Deploy Federated Learning models on IoT devices for decentralized AI processing.
• Minimize latency and enhance real-time decision-making in edge computing environments.
• Address challenges associated with data privacy and network constraints in IoT systems.

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.

In this instructor-led live training in Kenya, participants will explore the various aspects of NB-IoT (also known as LTE Cat NB1) while developing and deploying a sample NB-IoT-based application.

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

• Identify the different components of NB-IoT and understand how they integrate to form an ecosystem.
• Understand and explain the security features built into NB-IoT devices.
• Develop a simple application to track NB-IoT devices.

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.

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.

TinyML represents a framework for deploying machine learning models on low-power, resource-constrained devices in the field.

This instructor-led, live training (available online or onsite) is designed for intermediate-level professionals who wish to apply TinyML techniques to smart agriculture solutions that enhance automation and environmental intelligence.

Upon completing this program, participants will gain the ability to:

• Build and deploy TinyML models for agricultural sensing applications.
• Integrate edge AI into IoT ecosystems for automated crop monitoring.
• Use specialized tools to train and optimize lightweight models.
• Develop workflows for precision irrigation, pest detection, and environmental analytics.

Format of the Course

• Guided presentations and applied technical discussion.
• Hands-on practice using real-world datasets and devices.
• Practical experimentation in a supported lab environment.

Course Customization Options

• For tailored training aligned with specific agricultural systems, please contact us to customize the program.