TinyML for IoT Applications Training Course

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 session in Czech Republic (online or onsite) targets intermediate-level IT professionals and business managers who wish 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:

• Understand the fundamental principles of IoT and edge computing and their role in digital transformation.
• Identify relevant use cases for IoT and edge computing in manufacturing, logistics, and energy sectors.
• Differentiate between edge and cloud computing architectures and their deployment contexts.
• Implement edge computing solutions for predictive maintenance and real-time decision-making.

This instructor-led, live training in Czech Republic (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 Czech Republic (online or on-site) is designed for product managers and developers who wish to use Edge Computing to decentralize data management for improved 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 Czech Republic (available online or onsite) is designed for intermediate-level professionals aiming 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 contexts.
• Deploy Federated Learning models on IoT devices to facilitate decentralized AI processing.
• Minimize latency and enhance real-time decision-making capabilities in edge computing environments.
• Overcome challenges associated with data privacy and network constraints in IoT systems.

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.

In this instructor-led live training in Czech Republic, participants will explore the various aspects of NB-IoT (also known as LTE Cat NB1) while developing and deploying a sample NB-IoT-based application.

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

• Identify the different components of NB-IoT and how they integrate to form an ecosystem.
• Understand and explain the security features built into NB-IoT devices.
• Develop a simple application to track NB-IoT devices.

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.

This instructor-led, live training in Czech Republic (online or onsite) targets intermediate-level embedded engineers, IoT developers, and AI researchers seeking to implement TinyML techniques for AI-powered applications on energy-efficient hardware.

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

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

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.

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.