TinyML for IoT Applications Training Course

TinyML involves deploying optimized machine learning models onto edge devices with limited resources.

This instructor-led live training, available online or onsite, targets advanced technical professionals who want to design, optimize, and deploy comprehensive TinyML pipelines.

Upon completing this training, participants will be able to:

• Gather, prepare, and manage datasets tailored for TinyML applications.
• Train and optimize models specifically for low-power microcontrollers.
• Transform models into lightweight formats ideal for edge devices.
• Deploy, test, and monitor TinyML applications on actual hardware.

Course Format

• Instructor-led lectures combined with technical discussions.
• Practical laboratory exercises 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 arrange a customized session.

This instructor-led, live training in Bulgaria (online or onsite) is designed for intermediate 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.

Upon completing this training, participants will be able to:

• Comprehend the core principles of IoT and edge computing and their significance in digital transformation.
• Recognize specific use cases for IoT and edge computing within the manufacturing, logistics, and energy industries.
• Distinguish between edge and cloud computing architectures, as well as their respective deployment scenarios.
• Deploy edge computing solutions to support predictive maintenance and real-time decision-making processes.

This instructor-led, live training in Bulgaria (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 Bulgaria (online or onsite) is designed for product managers and developers who aim to utilize Edge Computing to decentralize data management for improved performance, capitalizing on smart devices situated at the source network.

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

• Understand the core concepts and advantages of Edge Computing.
• Identify use cases and examples suitable for Edge Computing application.
• Design and build Edge Computing solutions to facilitate faster data processing and lower operational costs.

This instructor-led, live training in Bulgaria (online or onsite) is tailored for intermediate-level professionals who wish to apply Federated Learning to optimize IoT and edge computing solutions.

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

• Understand the principles and benefits of Federated Learning in IoT and edge computing.
• Implement Federated Learning models on IoT devices for decentralized AI processing.
• Reduce latency and improve real-time decision-making in edge computing environments.
• Address challenges related to data privacy and network constraints in IoT systems.

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

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

• Grasp the fundamentals of TinyML and its advantages for edge AI applications.
• Configure 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.
• Enhance AI models for better power efficiency and memory utilization.

In this instructor-led, live training in Bulgaria, 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 to fit together 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 on hardware with severely limited resources.

This instructor-led live training, available online or onsite, is designed for advanced practitioners seeking to optimize TinyML models for low-latency, memory-efficient deployment on embedded devices.

Upon completing this training, participants will be able to:

• Utilize quantization, pruning, and compression techniques to minimize model size while preserving 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.

Course Format

• Instructor-led presentations complemented by technical demonstrations.
• Practical optimization exercises and comparative performance testing.
• Hands-on implementation of TinyML pipelines within a controlled lab environment.

Course Customization Options

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

TinyML refers to the deployment of machine learning models on low-power, resource-constrained devices 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 implement privacy-preserving techniques in edge AI applications.

Upon completion 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.
• Harden TinyML models and embedded systems against adversarial threats.
• Apply best practices for secure data handling in constrained environments.

Format of the Course

• Engaging lectures supported by expert-led discussions.
• Practical exercises emphasizing real-world threat scenarios.
• Hands-on implementation using embedded security and TinyML tooling.

Course Customization Options

• Organizations may request a tailored version of this training to align with their specific security and compliance needs.

ThingsBoard is an open-source IoT platform that provides device management, data collection, processing, and visualization for your IoT solution.

In this instructor-led live training, participants will learn how to integrate ThingsBoard into their IoT solutions.

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

• Install and configure ThingsBoard
• Understand the fundamentals of ThingsBoard features and architecture
• Build IoT applications using ThingsBoard
• Integrate ThingsBoard with Kafka for telemetry device data routing
• Integrate ThingsBoard with Apache Spark for data aggregation from multiple devices

Audience

• Software engineers
• Hardware engineers
• Developers

Format of the course

• Part lecture, part discussion, exercises, and heavy hands-on practice

Note

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

This instructor-led, live training in Bulgaria (online or onsite) is designed for beginner-level engineers and data scientists who want to understand TinyML fundamentals, 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 represents a framework designed for deploying machine learning models on low-power microcontrollers and embedded platforms, particularly within the realms of robotics and autonomous systems.

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

After completing this course, participants will be equipped to:

• Design optimized TinyML models tailored for robotics applications.
• Implement on-device perception pipelines to enable real-time autonomy.
• Integrate TinyML into established robotic control frameworks.
• Deploy and evaluate lightweight AI models on embedded hardware platforms.

Course Format

• Technical lectures paired with interactive discussions.
• Hands-on labs centered on embedded robotics tasks.
• Practical exercises that simulate real-world autonomous workflows.

Customization Options

• For organizations with specific robotics environments, customization can be arranged upon request.

This instructor-led, live training in Bulgaria (online or onsite) is designed for intermediate-level embedded engineers, IoT developers, and AI researchers who aim to implement 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.
• Implementing lightweight AI models on microcontrollers.
• Enhancing AI inference for minimal power usage.
• Incorporating TinyML into practical IoT solutions.

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

This instructor-led training, available online or onsite, targets intermediate-level practitioners looking to implement TinyML solutions for healthcare monitoring and diagnostic purposes.

Upon completing this training, participants will be equipped to:

• Design and deploy TinyML models for real-time health data analysis.
• Collect, preprocess, and interpret biosensor data to generate AI-driven insights.
• Optimize models for wearable devices with limited power and memory.
• Assess the clinical relevance, reliability, and safety of TinyML outputs.

Course Format

• Lectures complemented by live demonstrations and interactive discussions.
• Practical exercises involving wearable device data and TinyML frameworks.
• Guided lab exercises for implementation.

Customization Options

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

TinyML represents a specialized machine learning methodology designed for devices with limited resources and compact form factors.

This instructor-led live training, available online or in-person, is tailored for learners at beginner to intermediate levels who aim to develop functional TinyML applications using Raspberry Pi, Arduino, and comparable microcontrollers.

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

• Gather and preprocess data specifically for TinyML initiatives.
• Train and refine compact machine learning models suitable for microcontroller environments.
• Deploy TinyML models on Raspberry Pi, Arduino, and related development boards.
• Create comprehensive embedded AI prototypes from start to finish.

Course Format

• Instructor-led presentations alongside guided discussions.
• Practical exercises and hands-on experimentation.
• Live lab projects utilizing actual hardware.

Customization Options

• For specialized training that aligns with your specific hardware requirements or use cases, please reach out to us to make arrangements.