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

This instructor-led, live training in Czech Republic (online or onsite) is designed for intermediate-level professionals in telecommunications, AI engineering, and IoT who want to explore how 5G networks accelerate Edge AI applications.

Upon completing this training, participants will be capable of:

• Grasping the core principles of 5G technology and its influence on Edge AI.
• Deploying AI models specifically optimized for low-latency requirements within 5G infrastructures.
• Building real-time decision-making systems that leverage Edge AI and 5G connectivity.
• Optimizing AI workloads to ensure efficient operation on edge devices.

6G and the Intelligent Edge is a forward-thinking course that investigates the convergence of 6G wireless technologies with edge computing, IoT ecosystems, and AI-powered data processing to enable intelligent, low-latency, and adaptive infrastructures.

This instructor-led, live training (available online or onsite) targets intermediate-level IT architects looking to understand and design next-generation distributed architectures by leveraging the synergy between 6G connectivity and intelligent edge systems.

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

• Grasp how 6G will reshape edge computing and IoT architectures.
• Design distributed systems supporting ultra-low latency, high bandwidth, and autonomous operations.
• Integrate AI and data analytics at the edge to facilitate intelligent decision-making.
• Plan scalable, secure, and resilient infrastructures prepared for 6G.
• Evaluate business and operational models enabled by the convergence of 6G and edge technologies.

Format of the Course

• Interactive lectures and discussions.
• Case studies and applied architecture design exercises.
• Hands-on simulation using optional edge or container tools.

Course Customization Options

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

This instructor-led, live training in Czech Republic (online or onsite) targets advanced-level AI practitioners, researchers, and developers aiming to master recent advancements in Edge AI, optimize their AI models for edge deployment, and explore specialized applications across diverse industries.

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

• Investigate advanced techniques for developing and optimizing Edge AI models.
• Apply modern strategies for deploying AI models on edge devices.
• Leverage specialized tools and frameworks for advanced Edge AI applications.
• Enhance the performance and efficiency of Edge AI solutions.
• Discover innovative use cases and emerging trends in Edge AI.
• Tackle advanced ethical and security challenges in Edge AI deployments.

This instructor-led live training in Czech Republic (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 involves the deployment of optimized machine learning models onto edge devices with limited resources.

This guided live training session (available online or on-site) is designed for advanced technical professionals aiming to design, optimize, and implement comprehensive TinyML pipelines.

By the end of this training, participants will gain the ability to:

• Gather, prepare, and manage datasets specifically for TinyML applications.
• Train and optimize models for low-power microcontrollers.
• Transform models into lightweight formats compatible with edge devices.
• Deploy, test, and monitor TinyML applications in real-world hardware setups.

Course Format

• Instructor-led lectures accompanied by technical discussions.
• 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 make arrangements.

This instructor-led, live training in Czech Republic (online or onsite) is designed for advanced cybersecurity professionals, AI engineers, and IoT developers who want to implement robust security measures and resilience strategies for Edge AI systems.

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

• Grasp the security risks and vulnerabilities associated with Edge AI deployments.
• Apply encryption and authentication techniques to safeguard data.
• Architect resilient Edge AI systems capable of withstanding cyber threats.
• Deploy AI models securely within edge environments.

This instructor-led, live training in Czech Republic (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 completing this training, participants will be able to:

• Grasp the core concepts of TinyML and understand its advantages for edge AI applications.
• Establish a development environment suitable for TinyML projects.
• Train, optimize, and deploy AI models on low-power microcontrollers.
• Utilize 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 severely limited resources.

This guided, live training session (available online or in-person) targets experienced practitioners seeking to optimize TinyML models for low-latency, memory-efficient operation on embedded devices.

After finishing this course, participants will be capable of:

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

Course Format

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

Customization Options

• For training tailored to specific hardware platforms or internal workflows, please reach out to customize the program.

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

This instructor-led, live training (available online or onsite) targets 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 implementations.
• Strengthen TinyML models and embedded systems against adversarial threats.
• Apply best practices for secure data handling in constrained environments.

Course Format

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

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 Czech Republic (online or onsite) 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 implement AI models on microcontrollers.

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

• Grasping the fundamentals of TinyML and recognizing its importance.
• Deploying lightweight AI models onto microcontrollers and edge devices.
• Optimizing and fine-tuning machine learning models to minimize power consumption.
• Applying TinyML to real-world scenarios such as gesture recognition, anomaly detection, and audio processing.

TinyML provides a framework for deploying machine learning models on low-power microcontrollers and embedded platforms utilized in robotics and autonomous systems.

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

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

• Design optimized TinyML models tailored 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.

Course Format

• Technical lectures supplemented by interactive discussions.
• Hands-on labs focusing on embedded robotics tasks.
• Practical exercises that simulate real-world autonomous workflows.

Course Customization Options

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

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

This instructor-led live training (available online or onsite) targets intermediate-level practitioners aiming 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-limited wearable devices.
• Evaluate the clinical relevance, reliability, and safety of TinyML-driven outputs.

Course Format

• Lectures supported by live demonstrations and interactive discussion.
• Hands-on practice with wearable device data and TinyML frameworks.
• Implementation exercises conducted in a guided lab environment.

Course Customization Options

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

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

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

• Grasp the core concepts of TinyML and its role in IoT.
• Establish a TinyML development environment for IoT initiatives.
• Create and deploy machine learning models on low-power microcontrollers.
• Apply TinyML for predictive maintenance and anomaly detection.
• Optimize TinyML models to maximize power efficiency and minimize memory usage.

TinyML is a machine learning approach optimized for small, resource-constrained devices.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level learners who wish to build working TinyML applications using Raspberry Pi, Arduino, and similar microcontrollers.

Upon completing this training, attendees will gain the skills to:

• Collect and prepare data for TinyML projects.
• Train and optimize small machine learning models for microcontroller environments.
• Deploy TinyML models on Raspberry Pi, Arduino, and related boards.
• Develop end-to-end embedded AI prototypes.

Format of the Course

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

Course Customization Options

• For tailored training aligned with 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 directly in the field.

This instructor-led live training (available online or onsite) targets intermediate-level professionals aiming to apply TinyML techniques to smart agriculture solutions, enhancing 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 for automated crop monitoring.
• Utilize specialized tools to train and optimize lightweight models.
• Develop workflows for precision irrigation, pest detection, and environmental analytics.

Course Format

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

Customization Options

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