TinyML: Running AI on Ultra-Low-Power Edge Devices 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.

This forward-thinking course delves into how 6G wireless technology converges with edge computing, IoT ecosystems, and AI-powered data processing to build intelligent, low-latency, and adaptive infrastructures.

Delivered via live, instructor-led sessions (available online or onsite), this program is designed for intermediate-level IT architects seeking to grasp and architect next-generation distributed systems by harnessing the combined power of 6G connectivity and intelligent edge platforms.

After completing this course, participants will be equipped to:

• Grasp how 6G will reshape edge computing and IoT architectures.
• Architect distributed systems capable of ultra-low latency, high bandwidth, and autonomous functions.
• Implement AI and data analytics at the edge to enable smart decision-making.
• Develop scalable, secure, and resilient 6G-ready edge infrastructures.
• Assess business and operational models that arise from the convergence of 6G and edge networks.

Course Format

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

Customization Options

• For tailored training arrangements for this course, please reach out to us.

This instructor-led, live training in Sweden (online or onsite) is aimed at advanced-level AI practitioners, researchers, and developers who wish to master the latest advancements in Edge AI, optimize their AI models for edge deployment, and explore specialized applications across various industries.

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

• Explore advanced techniques in Edge AI model development and optimization.
• Implement cutting-edge strategies for deploying AI models on edge devices.
• Utilize specialized tools and frameworks for advanced Edge AI applications.
• Optimize performance and efficiency of Edge AI solutions.
• Explore innovative use cases and emerging trends in Edge AI.
• Address advanced ethical and security considerations in Edge AI deployments.

This instructor-led, live training in Sweden (online or onsite) is aimed at intermediate-level developers, data scientists, and tech enthusiasts who wish to gain practical skills in deploying AI models on edge devices for various applications.

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

• Understand the principles of Edge AI and its benefits.
• Set up and configure the edge computing environment.
• Develop, train, and optimize AI models for edge deployment.
• Implement practical AI solutions on edge devices.
• Evaluate and improve the performance of edge-deployed models.
• Address ethical and security considerations in Edge AI applications.

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 aimed at advanced-level cybersecurity professionals, AI engineers, and IoT developers who wish to implement robust security measures and resilience strategies for Edge AI systems.

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

• Understand security risks and vulnerabilities in Edge AI deployments.
• Implement encryption and authentication techniques for data protection.
• Design resilient Edge AI architectures that can withstand cyber threats.
• Apply secure AI model deployment strategies in edge environments.

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.

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.

This instructor-led, live training in Sweden (online or onsite) targets intermediate-level IoT developers, embedded engineers, and AI practitioners who wish to implement TinyML for predictive maintenance, anomaly detection, and smart sensor applications.

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

• Grasp the core principles of TinyML and its role in IoT ecosystems.
• Establish a TinyML development environment tailored for IoT projects.
• Create and deploy ML models on low-power microcontrollers.
• Apply TinyML to achieve predictive maintenance and anomaly detection.
• Optimize TinyML models to ensure efficient power and memory utilization.

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.