TinyML for IoT Applications Training Course

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

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

• Understand the fundamentals of 5G technology and its impact on Edge AI.
• Deploy AI models optimized for low-latency applications in 5G environments.
• Implement real-time decision-making systems using Edge AI and 5G connectivity.
• Optimize AI workloads for efficient performance on edge devices.

This forward-looking course investigates how 6G wireless technologies merge with edge computing, IoT ecosystems, and AI-powered data processing to build intelligent, adaptive, and low-latency infrastructures.

Delivered as live, instructor-led training (available online or onsite), this programme is tailored for intermediate-level IT architects looking to comprehend and architect next-generation distributed systems that harness the combined power of 6G connectivity and intelligent edge platforms.

After completing this course, participants will be equipped to:

• Grasp how 6G is set to revolutionize edge computing and IoT architectures.
• Architect distributed systems optimized for ultra-low latency, high bandwidth, and autonomous functions.
• Embed AI and data analytics at the edge to facilitate intelligent decision-making.
• Plan robust, secure, and scalable 6G-ready edge infrastructures.
• Assess the business and operational models unlocked by the convergence of 6G and edge networks.

Course Format

• Interactive lectures and group discussions.
• Case studies alongside practical architecture design exercises.
• Hands-on simulations using optional edge or container-based tools.

Course Customization Options

• To arrange bespoke training for this course, please get in touch with us.

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

This guided, live training session (available online or on-site) targets advanced technical professionals looking to design, optimize, and deploy complete TinyML pipelines.

Upon completing this course, participants will be able to:

• Gather, prepare, and manage datasets for TinyML applications.
• Train and optimize models specifically for low-power microcontrollers.
• Convert models into lightweight formats suitable for edge devices.
• Deploy, test, and monitor TinyML applications on actual hardware.

Course Format

• Guided lectures and technical discussions led by an instructor.
• 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 workflows, please contact us to arrange.

This instructor-led live training in the Netherlands (online or onsite) is targeted 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 across various industries.

Upon completion of this training, participants will be capable of:

• Grasping the core principles of IoT and edge computing and their significance in digital transformation.
• Recognizing use cases for IoT and edge computing within the manufacturing, logistics, and energy sectors.
• Differentiating between edge and cloud computing architectures and deployment models.
• Deploying edge computing solutions to facilitate predictive maintenance and real-time decision-making.

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

Upon completing this training, participants will be capable of:

• Grasping the core principles of TinyML and its advantages for edge AI applications.
• Configuring a development environment suitable for TinyML projects.
• Training, optimizing, and deploying AI models on low-power microcontrollers.
• Leveraging TensorFlow Lite and Edge Impulse to build practical TinyML solutions.
• Enhancing AI models for improved power efficiency and adherence to memory limitations.

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 professionals looking to optimize TinyML models for low-latency and memory-efficient execution on embedded devices.

After finishing this course, participants will be capable of:

• Utilizing quantization, pruning, and compression methods to decrease model size while maintaining accuracy.
• Assessing TinyML models for latency, memory usage, and energy efficiency.
• Deploying optimized inference pipelines on microcontrollers and edge devices.
• Analyzing the trade-offs between performance, accuracy, and hardware limitations.

Course Format

• Instructor-led lectures complemented by technical demonstrations.
• Practical exercises focused on optimization and comparative performance testing.
• Hands-on creation of TinyML pipelines within a controlled lab setting.

Customization Options for the Course

• To receive training tailored to specific hardware platforms or internal processes, please reach out to us for program customization.

TinyML involves deploying machine learning models on 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 capable of:

• Recognizing security risks specific to on-device TinyML inference.
• Deploying privacy-preserving mechanisms for edge AI solutions.
• Strengthening TinyML models and embedded systems against adversarial threats.
• Applying best practices for secure data management in constrained environments.

Course Format

• Interactive lectures complemented by expert-led discussions.
• Practical exercises focused on real-world threat scenarios.
• Hands-on implementation using embedded security and TinyML tools.

Customization Options

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

This guided, live training in the Netherlands (online or in person) is designed for engineers and data scientists at the beginner level who want to grasp the core principles of TinyML, investigate its practical uses, and deploy AI models on microcontrollers.

Upon completion of this training, participants will be capable of:

• Grasping the core concepts of TinyML and its importance.
• Deploying compact AI models on microcontrollers and edge devices.
• Optimizing and refining machine learning models to minimize power usage.
• Utilizing TinyML for real-world scenarios such as gesture recognition, anomaly detection, and audio processing.

TinyML represents a framework designed to deploy machine learning models onto low-power microcontrollers and embedded platforms utilized in robotics and autonomous systems.

This instructor-led, live training (available online or onsite) is tailored for advanced-level 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 able to:

• Design optimized TinyML models specifically for robotics applications.
• Implement on-device perception pipelines to enable real-time autonomy.
• Integrate TinyML solutions 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 Netherlands (online or onsite) is designed for intermediate-level embedded engineers, IoT developers, and AI researchers who wish to apply TinyML techniques for AI-enabled applications on energy-efficient hardware.

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

• Grasp the core concepts of TinyML and edge AI.
• Deploy lightweight AI models onto microcontrollers.
• Optimize AI inference to minimize power consumption.
• Integrate TinyML solutions into practical IoT applications.

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

This instructor-led live training (available online or onsite) is designed for intermediate-level practitioners looking 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 generate AI-driven insights.
• Optimize models for low-power and memory-limited wearable devices.
• Evaluate the clinical relevance, reliability, and safety of TinyML outputs.

Course Format

• Lectures accompanied by live demonstrations and interactive discussions.
• Practical exercises using wearable device data and TinyML frameworks.
• Implementation tasks within a guided lab environment.

Customization Options

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

TinyML represents a machine learning methodology specifically optimized for devices with limited resources and compact form factors.

This live training, led by an instructor, is available both online and on-site. It is designed for learners ranging from beginner to intermediate levels who aim to create functional TinyML applications using Raspberry Pi, Arduino, and comparable microcontrollers.

After completing this course, participants will be able to:

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

Course Format

• Instructor-led presentations combined with guided discussions.
• Practical exercises and hands-on experimentation.
• Real-lab project work conducted on actual hardware.

Customization Options

• For bespoke training tailored to your specific hardware requirements or use cases, please contact us to arrange a session.

TinyML enables the deployment of machine learning models on low-power, resource-constrained devices directly in the field.

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

Upon completing this program, participants will be able to:

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

Format of the Course

• Guided presentations coupled with applied technical discussions.
• Hands-on practice using real-world datasets and devices.
• Practical experimentation within a supported lab environment.

Course Customization Options

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