TinyML for IoT Applications Training Course

This instructor-led, live training in Nigeria (online or onsite) is designed for intermediate-level telecom professionals, AI engineers, and IoT specialists eager to discover how 5G networks accelerate Edge AI applications.

Upon completing this training, participants will be able to:

• Grasp the fundamentals of 5G technology and its influence on Edge AI.
• Deploy AI models optimized for low-latency applications within 5G environments.
• Implement real-time decision-making systems leveraging Edge AI and 5G connectivity.
• Optimize AI workloads to ensure efficient performance on edge devices.

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

This instructor-led live training session, available both online and onsite, is designed for senior technical professionals seeking to design, optimize, and implement complete TinyML pipelines.

Upon completing this training, participants will be able to:

• Gather, prepare, and manage datasets specifically for TinyML applications.
• Train and optimize models to run efficiently on low-power microcontrollers.
• Transform models into lightweight formats compatible with edge devices.
• Deploy, test, and monitor TinyML applications in real-world hardware setups.
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Course Format

• Lectures guided by an instructor, supplemented by technical discussions.
• Practical laboratory sessions and iterative experimentation.
• Hands-on deployment exercises on microcontroller-based platforms.
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Course Customization Options

• For tailored training that incorporates specific toolchains, hardware boards, or internal workflows, please contact us to make arrangements.
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This instructor-led, live training in Nigeria (online or onsite) is designed for 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 Nigeria (available online or onsite) targets intermediate-level developers, system architects, and industry professionals who wish to enhance IoT applications with intelligent data processing and analytics capabilities using Edge AI.

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 Nigeria (online or onsite) is aimed at product managers and developers who wish to use Edge Computing to decentralise 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 Nigeria (online or onsite) is designed for intermediate-level professionals who wish to apply Federated Learning to optimize IoT and edge computing solutions.

Upon completing this training, participants will be capable of:

• Grasping the principles and advantages of Federated Learning in IoT and edge computing.
• Deploying Federated Learning models on IoT devices to enable decentralized AI processing.
• Minimizing latency and enhancing real-time decision-making in edge computing settings.
• Tackling challenges associated with data privacy and network limitations in IoT systems.

This instructor-led, live training in Nigeria (online or onsite) is designed for intermediate-level embedded systems engineers and AI developers who want to deploy machine learning models on microcontrollers using TensorFlow Lite and Edge Impulse.

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

• Grasp the fundamentals of TinyML and its advantages for edge AI applications.
• Configure a development environment suitable for TinyML projects.
• Train, optimize, and deploy AI models on low-power microcontrollers.
• Utilize TensorFlow Lite and Edge Impulse to build real-world TinyML applications.
• Optimize AI models to meet power efficiency and memory constraints.

TinyML involves deploying machine learning models on hardware with severely limited resources.

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

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

• Utilize quantization, pruning, and compression techniques to minimize model size while maintaining accuracy.
• Benchmark TinyML models to assess latency, memory usage, and energy efficiency.
• Implement optimized inference pipelines on microcontrollers and edge devices.
• Evaluate the trade-offs between performance, accuracy, and hardware limitations.

Course Format

• Instructor-led presentations complemented by technical demonstrations.
• Practical optimization exercises and comparative performance testing.
• Hands-on implementation of TinyML pipelines in 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 on low-power, resource-constrained devices operating at the network edge.

This instructor-led live training (available online or onsite) is designed for advanced-level 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 Nigeria (online or on-site) is designed for beginner-level engineers and data scientists who wish to understand TinyML fundamentals, explore its applications, and deploy AI models on microcontrollers.

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

• Understand the fundamentals of TinyML and its significance.
• Deploy lightweight AI models on microcontrollers and edge devices.
• Optimize and fine-tune machine learning models for low-power consumption.
• Apply TinyML for real-world applications 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 either online or onsite, is designed for advanced professionals seeking to integrate perception and decision-making capabilities based on TinyML into autonomous robots, drones, and intelligent control systems.

After completing this course, participants will be capable of:

• Designing optimized TinyML models tailored for robotics applications.
• Implementing on-device perception pipelines to enable real-time autonomy.
• Integrating TinyML into existing robotic control frameworks.
• Deploying and testing lightweight AI models on embedded hardware platforms.

Format of the Course

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

Course Customization Options

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

This instructor-led, live training in Nigeria (online or onsite) targets intermediate-level embedded engineers, IoT developers, and AI researchers who wish to implement TinyML techniques for AI-powered applications on energy-efficient hardware.

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

• Understand the fundamentals of TinyML and edge AI.
• Deploy lightweight AI models on microcontrollers.
• Optimize AI inference for low-power consumption.
• Integrate TinyML with real-world IoT applications.

TinyML involves embedding machine learning capabilities into wearable and medical devices that operate with low power and limited resources.

This instructor-led live training, available either online or onsite, is designed for intermediate-level professionals aiming 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 processing health data in real-time.
• Collect, preprocess, and interpret biosensor data to derive AI-driven insights.
• Optimize models to run efficiently on wearable devices with constrained memory and power.
• 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 implementation exercises within a lab environment.

Customization Options

• For specialized training that aligns with specific healthcare devices or regulatory workflows, please reach out to us to tailor 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 enables the deployment of machine learning models on low-power, resource-constrained devices in the field.

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

Upon completing this programme, participants will be able 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.

Course Format

• 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 programme.