TinyML for IoT Applications Training Course

This instructor-led, live training in Sweden (online or onsite) is designed for intermediate-level telecom professionals, AI engineers, and IoT specialists interested in exploring how 5G networks enhance Edge AI applications.

By the end of 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 for efficient performance on edge devices.

TinyML refers to the implementation of streamlined machine learning models on edge devices with limited resources.

This instructor-led training session (available online or in-person) targets advanced technical professionals aiming to design, refine, and deploy comprehensive TinyML pipelines.

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

• Gather, preprocess, and manage datasets tailored for TinyML solutions.
• Train and optimize models for energy-efficient microcontrollers.
• Transform models into compact formats ideal for edge devices.
• Deploy, test, and oversee TinyML applications on physical hardware.

Course Format

• Instructor-led lectures and technical discourse.
• Practical laboratory sessions and iterative experimentation.
• Direct deployment exercises on microcontroller platforms.

Customization Options

• To tailor the training with specific toolchains, hardware boards, or internal processes, please contact us to make arrangements.

This instructor-led live training in Sweden (online or onsite) is designed for intermediate IT professionals and business managers seeking to understand how IoT and edge computing can drive efficiency, real-time processing, and innovation across various industries.

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

• Comprehend 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 Sweden (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 Sweden (online or onsite) is aimed at 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 Sweden (online or onsite) is designed for intermediate-level professionals seeking to apply Federated Learning to optimize IoT and edge computing solutions.

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

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

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

Upon completing 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 solutions.
• Optimize AI models to meet power efficiency and memory limitations.

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

This guided, live training session (available online or at your location) is designed for advanced practitioners seeking to optimize TinyML models for low-latency and memory-efficient operation on embedded devices.

After completing this course, participants will be capable of:

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

Course Format

• Guided presentations complemented by technical demonstrations.
• Practical exercises in optimization and comparative performance analysis.
• Hands-on development of TinyML pipelines within a controlled lab setting.

Customization Options

• For training tailored to specific hardware platforms or internal processes, please reach out to us to customize the curriculum.

TinyML represents a methodology for deploying machine learning models onto low-power devices with limited resources, operating at the network edge.

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

Upon completing this course, participants will be equipped to:

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

Course Customization Options

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

This live, instructor-led training located in Sweden (available online or on-site) is designed for beginner-level engineers and data scientists who want to learn the basics of TinyML, explore its applications, and deploy AI models on microcontrollers.

Upon completing 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 enables the deployment of machine learning models on low-power microcontrollers and embedded platforms, which are essential for robotics and autonomous systems.

This instructor-led live training, available online or onsite, is designed for advanced professionals aiming to incorporate TinyML-based perception and decision-making capabilities into autonomous robots, drones, and intelligent control systems.

Upon completing this course, participants will be able to:

• Create optimized TinyML models tailored for robotic applications.
• Build on-device perception pipelines to support real-time autonomy.
• Integrate TinyML solutions into established robotic control frameworks.
• Deploy and evaluate lightweight AI models on embedded hardware.

Course Format

• Technical lectures accompanied by interactive discussions.
• Practical labs focused on embedded robotics tasks.
• Real-world autonomous workflow simulations through hands-on exercises.

Customization Options

• Customization for organization-specific robotics environments is available upon request.

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

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

• Grasping the core principles of TinyML and edge AI.
• Deploying lightweight AI models onto microcontrollers.
• Optimizing AI inference to minimize power consumption.
• Integrating TinyML with practical IoT applications.

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

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

Upon completing 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-constrained wearable devices.
• Evaluate the clinical relevance, reliability, and safety of outputs generated by TinyML.

Course Format

• Lectures complemented by live demonstrations and interactive discussions.
• Practical exercises using wearable device data and TinyML frameworks.
• Guided implementation exercises within a lab environment.

Customization Options

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

TinyML represents a machine learning methodology fine-tuned for compact devices with limited resources.

This instructor-led training, available online or on-site, targets beginner to intermediate learners aiming to develop functional TinyML applications using Raspberry Pi, Arduino, and comparable microcontrollers.

Upon completion of this training, participants will acquire the ability to:

• Gather and preprocess data for TinyML initiatives.
• Train and refine small-scale machine learning models for microcontroller environments.
• Deploy TinyML models on Raspberry Pi, Arduino, and associated boards.
• Create comprehensive embedded AI prototypes.

Course Format

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

Course Customization Options

• For personalized training tailored to 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 operating directly in the field.

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

Upon completing this program, participants will be able to:

• Construct and deploy TinyML models tailored for agricultural sensing applications.
• Integrate edge AI into IoT ecosystems for automated crop monitoring.
• Utilize 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.