TinyML for IoT Applications Training Course

TinyML involves deploying optimized machine learning models on edge devices with limited resources.

This instructor-led training session, available online or onsite, is designed for advanced technical professionals looking to design, optimize, and implement comprehensive TinyML pipelines.

Upon completing this course, participants will be able to:

• Gather, prepare, and manage datasets specifically for TinyML applications.
• Train and fine-tune models to run efficiently on low-power microcontrollers.
• Transform models into lightweight formats ideal for edge devices.
• Deploy, test, and oversee TinyML applications in live hardware setups.

Course Delivery Format

• Guided lectures and in-depth technical discussions led by the instructor.
• Practical laboratory sessions and iterative experimentation.
• Direct deployment exercises on microcontroller-based platforms.

Customization Options

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

This instructor-led, live training in the UAE (online or onsite) is designed for intermediate-level IT professionals and business managers who wish to explore the potential of IoT and edge computing to enhance efficiency, enable real-time processing, and drive innovation across various sectors.

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

• Understand the core principles of IoT and edge computing and their role in digital transformation.
• Identify relevant use cases for IoT and edge computing in manufacturing, logistics, and energy.
• 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 the UAE (online or onsite) is aimed at intermediate-level developers, system architects, and industry professionals who wish to leverage Edge AI for enhancing 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 session in the UAE (available online or onsite) is intended for product managers and developers who aim to decentralize data management to improve performance by leveraging smart devices located on the source network.

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

• Grasp the basic concepts and advantages of Edge Computing.
• Identify specific use cases and examples suitable for Edge Computing applications.
• Design and build Edge Computing solutions to enable faster data processing and reduce operational costs.

This instructor-led, live training in the UAE (online or onsite) targets intermediate-level professionals aiming to apply Federated Learning to optimize IoT and edge computing solutions.

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

• Understand the principles and benefits of Federated Learning in IoT and edge computing.
• Implement Federated Learning models on IoT devices for decentralized AI processing.
• Reduce latency and improve real-time decision-making in edge computing environments.
• Address challenges related to data privacy and network constraints in IoT systems.

This instructor-led, live training in the UAE (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.

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

• Grasp the fundamentals of TinyML and its advantages for edge AI applications.
• Configure a development environment tailored for TinyML projects.
• Train, optimize, and deploy AI models on low-power microcontrollers.
• Leverage TensorFlow Lite and Edge Impulse to build real-world TinyML applications.
• Enhance AI models for improved power efficiency and adherence to memory limitations.

In this instructor-led, live training in the UAE, 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 deploying machine learning models on hardware with severe resource constraints.

This instructor-led live training (available online or on-site) 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:

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

Course Format

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

Customization Options

• For customized training aligned with specific hardware platforms or internal workflows, please contact us.

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

This instructor-led, live training—available either online or on-site—is designed for advanced-level professionals aiming to secure TinyML pipelines and implement privacy-preserving techniques within edge AI applications.

Upon completing this course, participants will be able to:

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

Customization Options

• Organizations can request a customized version of this training to align with their specific security and compliance requirements.

ThingsBoard is an open-source IoT platform that provides device management, data collection, processing, and visualization for your IoT solutions.

Through this instructor-led live training, participants will learn how to integrate ThingsBoard into their IoT solutions.

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

• Install and configure ThingsBoard
• Understand the core features and architecture of ThingsBoard
• Develop IoT applications using ThingsBoard
• Integrate ThingsBoard with Kafka for routing telemetry data from devices
• Integrate ThingsBoard with Apache Spark for aggregating data from multiple devices

Audience

• Software engineers
• Hardware engineers
• Developers

Course Format

• A mix of lectures, discussions, exercises, and extensive hands-on practice

Note

• To request customized training for this course, please contact us to arrange.

This instructor-led, live training held in the UAE (available online or onsite) is tailored for beginner-level engineers and data scientists eager 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 onto low-power microcontrollers and embedded platforms, which are essential components in robotics and autonomous systems.

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

Upon completing this course, participants will be equipped to:

• Design optimized TinyML models tailored for robotics applications.
• Implement on-device perception pipelines to enable real-time autonomy.
• Integrate TinyML seamlessly 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 organization-specific robotics environments, customization can be arranged upon request.

This instructor-led, live training in the UAE (online or onsite) is designed for intermediate-level embedded engineers, IoT developers, and AI researchers aiming to implement TinyML techniques for AI-driven applications on energy-efficient hardware.

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

• Grasp the core principles of TinyML and edge AI.
• Deploy lightweight AI models on microcontrollers.
• Optimize AI inference to minimize power consumption.
• Integrate TinyML solutions into real-world IoT applications.

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

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

Upon completion of 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 for low-power and memory-constrained wearable devices.
• Assess the clinical relevance, reliability, and safety of TinyML-driven outputs.

Format of the Course

• Lectures supplemented by live demonstrations and interactive discussions.
• Hands-on practice with wearable device data and TinyML frameworks.
• Implementation exercises within a guided lab environment.

Course Customization Options

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

TinyML represents a specialized machine learning methodology designed to operate efficiently on compact, resource-limited devices.

This live training session, led by an instructor and available either online or in-person, is tailored for learners ranging from beginner to intermediate levels who aim to create functional TinyML applications using Raspberry Pi, Arduino, and comparable microcontrollers.

After finishing this course, participants will acquire the ability to:

• Gather and preprocess data specifically for TinyML initiatives.
• Train and refine small-scale machine learning models suitable for microcontroller environments.
• Deploy TinyML models onto Raspberry Pi, Arduino, and associated hardware boards.
• Construct complete embedded AI prototypes from start to finish.

Course Delivery Format

• Instructor-led lectures accompanied by guided discussions.
• Practical exercises and hands-on experimentation sessions.
• Live laboratory projects utilizing actual hardware.

Customization Availability

• For training programs customized to your specific hardware requirements or use cases, please get in touch to arrange a session.