TinyML for IoT Applications Training Course

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

This instructor-led live training, available online or in-person, is designed for advanced technical professionals seeking to design, optimize, and deploy comprehensive TinyML workflows.

Upon completing this training, participants will be able to:

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

Course Format

• Instructor-led lectures and 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 your customized session.

This instructor-led, live training in Norway (online or onsite) targets intermediate-level IT professionals and business managers who want to understand how IoT and edge computing can foster efficiency, real-time processing, and innovation across different industries.

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

• Understand the core principles of IoT and edge computing and their part in digital transformation.
• Identify practical applications for IoT and edge computing in manufacturing, logistics, and energy.
• Differentiate between edge and cloud computing architectures and deployment contexts.
• Implement edge computing solutions to facilitate predictive maintenance and real-time decision-making.

This instructor-led, live training in Norway (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 Norway (online or onsite) is designed for product managers and developers seeking to decentralize data management for improved performance, by leveraging smart devices located on the source network.

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

• Grasp the fundamental concepts and benefits of Edge Computing.
• Recognize practical use cases and real-world examples of Edge Computing applications.
• Design and implement Edge Computing solutions to accelerate data processing and lower operational costs.

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

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

• Grasp the core principles of TinyML and its advantages for edge AI solutions.
• Establish a development environment suitable for TinyML initiatives.
• Training, refining, and deploying AI models on power-efficient microcontrollers.
• Utilizing TensorFlow Lite and Edge Impulse to build practical TinyML applications.
• Enhancing AI models for improved power efficiency and to navigate memory limitations.

In this instructor-led, live training in Norway, participants will explore various aspects of NB-IoT (also known as LTE Cat NB1) as they develop and deploy 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 understand how they 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 deploying machine learning models on hardware with severe resource limitations.

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

Upon completing this training, participants will be able to:

• Utilize quantization, pruning, and compression techniques to reduce model size while maintaining accuracy.
• Benchmark TinyML models to assess latency, memory usage, and energy efficiency.
• Implement optimized inference pipelines on microcontrollers and edge devices.
• Evaluate the trade-offs between performance, accuracy, and hardware constraints.

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 tailored training that aligns with specific hardware platforms or internal workflows, please contact us to customize the program.

TinyML involves deploying machine learning models onto low-power, resource-constrained devices operating at the network edge.

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

Upon completing this course, participants will be able to:

• Identify security risks unique 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 capabilities for your IoT solutions.

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

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

• Install and configure ThingsBoard
• Understand the core principles of ThingsBoard's features and architecture
• Develop IoT applications using ThingsBoard
• Integrate ThingsBoard with Kafka to route telemetry data from devices
• Integrate ThingsBoard with Apache Spark to aggregate data from multiple devices

Audience

• Software engineers
• Hardware engineers
• Developers

Course format

• A mix of lectures, discussions, exercises, and extensive hands-on practice

Note

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

This instructor-led, live training in Norway (online or onsite) is designed for beginner-level engineers and data scientists who want to grasp the core principles of TinyML, investigate its practical uses, and deploy AI models onto microcontrollers.

Upon completing this training, participants will be able to:

• Comprehend the basics of TinyML and its importance.
• Deploy lightweight AI models on microcontrollers and edge devices.
• Optimize and fine-tune machine learning models to minimize power usage.
• Implement TinyML in real-world scenarios such as gesture recognition, anomaly detection, and audio processing.

TinyML serves as a framework for deploying machine learning models on low-power microcontrollers and embedded platforms within robotics and autonomous systems.

This instructor-led live training, available online or onsite, is designed for advanced professionals looking 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 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.

Format of the Course

• Technical lectures combined with interactive discussions.
• Hands-on labs focusing on embedded robotics tasks.
• Practical exercises simulating real-world autonomous workflows.

Course Customization Options

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

This instructor-led, live training in Norway (online or onsite) is designed for intermediate-level embedded engineers, IoT developers, and AI researchers who want to implement TinyML techniques for AI-powered applications on energy-efficient hardware.

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

• Understand the fundamentals of TinyML and edge AI.
• Deploy lightweight AI models on microcontrollers.
• Optimize AI inference for low-power consumption.
• Integrate TinyML with real-world IoT applications.

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 practitioners aiming to implement TinyML solutions for healthcare monitoring and diagnostic applications.

Upon completing this training, participants will be capable of:

• Designing and deploying TinyML models for real-time health data processing.
• Collecting, preprocessing, and interpreting biosensor data to derive AI-driven insights.
• Optimizing models for low-power and memory-constrained wearable devices.
• Assessing the clinical relevance, reliability, and safety of TinyML-generated outputs.

Course Format

• Lectures complemented by live demonstrations and interactive discussions.
• Practical exercises involving wearable device data and TinyML frameworks.
• Implementation tasks conducted within a guided laboratory environment.

Customization Options

• For training tailored to 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.