TinyML: Running AI on Ultra-Low-Power Edge Devices Training Course

This instructor-led, live training in Slovakia (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 Slovakia (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 is the practice of deploying optimized machine learning models on resource-constrained edge devices.

This instructor-led, live training (online or onsite) is aimed at advanced-level technical professionals who wish to design, optimize, and deploy complete TinyML pipelines.

By the end of this training, participants will learn how to:

• Collect, prepare, and manage datasets for TinyML applications.
• Train and optimize models for low-power microcontrollers.
• Convert models to lightweight formats suitable for edge devices.
• Deploy, test, and monitor TinyML applications in real-world hardware environments.

Format of the Course

• Instructor-guided lectures and technical discussions.
• Practical labs and iterative experimentation.
• Hands-on deployment on microcontroller-based platforms.

Course 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 Slovakia (online or onsite) is aimed at intermediate-level developers and IT professionals who wish to gain a comprehensive understanding of Edge AI from concept to practical implementation, including setup and deployment.

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

• Understand the fundamental concepts of Edge AI.
• Set up and configure Edge AI environments.
• Develop, train, and optimize Edge AI models.
• Deploy and manage Edge AI applications.
• Integrate Edge AI with existing systems and workflows.
• Address ethical considerations and best practices in Edge AI implementation.

This instructor-led, live training in Slovakia (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 Slovakia (online or onsite) is aimed at beginner-level developers and IT professionals who wish to understand the fundamentals of Edge AI and its introductory applications.

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

• Understand the basic concepts and architecture of Edge AI.
• Set up and configure Edge AI environments.
• Develop and deploy simple Edge AI applications.
• Identify and understand the use cases and benefits of Edge AI.

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

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

• Understand the fundamentals of TinyML and its benefits for edge AI applications.
• Set up a development environment for TinyML projects.
• Train, optimize, and deploy AI models on low-power microcontrollers.
• Use TensorFlow Lite and Edge Impulse to implement real-world TinyML applications.
• Optimize AI models for power efficiency and memory constraints.

TinyML is the practice of deploying machine learning models on highly resource-constrained hardware.

This instructor-led, live training (online or onsite) is designed for advanced-level practitioners who aim to optimize TinyML models for low-latency and memory-efficient deployment on embedded devices.

Upon completing this training, participants will be able to:

• Apply quantization, pruning, and compression techniques to minimize model size without compromising accuracy.
• Benchmark TinyML models for latency, memory usage, and energy efficiency.
• Implement optimized inference pipelines on microcontrollers and edge devices.
• Assess the trade-offs between performance, accuracy, and hardware limitations.

Format of the Course

• 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.

Course Customization Options

• For customized training aligned with specific hardware platforms or internal processes, please contact us to tailor the program.

TinyML is an approach to deploying machine learning models on low-power, resource-constrained devices operating at the network edge.

This instructor-led, live training (online or onsite) is designed for advanced professionals who want to secure TinyML pipelines and implement privacy-preserving techniques in edge AI applications.

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

• Identify security risks specific to on-device TinyML inference.
• Implement privacy-preserving mechanisms for edge AI deployments.
• Strengthen TinyML models and embedded systems against adversarial threats.
• Apply best practices for secure data handling in resource-constrained environments.

Format of the Course

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

Course Customization Options

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

This instructor-led, live training in Slovakia (online or onsite) is aimed at 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 is a framework designed for deploying machine learning models on low-power microcontrollers and embedded platforms commonly used in robotics and autonomous systems.

This instructor-led, live training (available both online and onsite) is tailored for advanced-level professionals who aim to integrate TinyML-based 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 achieve real-time autonomy.
• Integrate TinyML into existing robotic control frameworks seamlessly.
• Deploy and test lightweight AI models on embedded hardware platforms effectively.

Format of the Course

• Technical lectures combined with interactive discussions to enhance understanding.
• Hands-on labs focusing on embedded robotics tasks to provide practical experience.
• Practical exercises that simulate real-world autonomous workflows to ensure readiness.

Course Customization Options

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

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

This instructor-led, live training (available online or on-site) is designed for intermediate-level practitioners who aim to implement TinyML solutions for healthcare monitoring and diagnostic applications.

After 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 derive AI-driven insights.
• Optimize models for low-power and memory-limited wearable devices.
• Assess the clinical relevance, reliability, and safety of TinyML-generated outputs.

Format of the Course

• Lectures supported by live demonstrations and interactive discussions.
• Hands-on practice with wearable device data and TinyML frameworks.
• Guided lab exercises for implementation.

Course Customization Options

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

This instructor-led, live training in Slovakia (online or onsite) is aimed at 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:

• Understand the fundamentals of TinyML and its applications in IoT.
• Set up a TinyML development environment for IoT projects.
• Develop and deploy ML models on low-power microcontrollers.
• Implement predictive maintenance and anomaly detection using TinyML.
• Optimize TinyML models for efficient power and memory usage.

TinyML is a machine learning methodology designed for small devices with limited resources.

This instructor-led, live training (available both online and on-site) is targeted at learners ranging from beginners to intermediates who are interested in developing functional TinyML applications using Raspberry Pi, Arduino, and similar microcontrollers.

By the end of this training, participants will have acquired the skills to:

• Gather and preprocess data for TinyML projects.
• Train and optimize compact machine learning models suitable for microcontroller environments.
• Deploy TinyML models on devices such as Raspberry Pi, Arduino, and related boards.
• Create complete embedded AI prototypes from start to finish.

Format of the Course

• Guided presentations by an instructor and interactive discussions.
• Practical exercises and hands-on experiments.
• Real-world project work using actual hardware in a live lab setting.

Course Customization Options

• For customized training that aligns with your specific hardware or use case, please contact us to arrange.

TinyML is a framework designed for deploying machine learning models on low-power, resource-constrained devices in various environments.

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

Upon completing this program, participants will be able to:

• Build and deploy TinyML models for agricultural sensing applications.
• Integrate edge AI into IoT ecosystems to facilitate automated crop monitoring.
• Use 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 discussions.
• 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.