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

This instructor-led, live training in Norway (online or onsite) is designed for intermediate-level telecom professionals, AI engineers, and IoT specialists keen on understanding how 5G networks accelerate Edge AI applications.

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

• Grasp the fundamentals of 5G technology and its influence on Edge AI.
• Deploy AI models tailored for low-latency applications within 5G environments.
• Implement real-time decision-making systems leveraging Edge AI and 5G connectivity.
• Optimize AI workloads to ensure efficient performance on edge devices.

6G and the Intelligent Edge is an innovative course that investigates the convergence of 6G wireless technologies, edge computing, IoT ecosystems, and AI-powered data processing to build intelligent, adaptive, and low-latency infrastructures.

This instructor-led live training (available online or onsite) targets intermediate-level IT architects looking to master and design next-generation distributed architectures that leverage the combined strengths of 6G connectivity and intelligent edge systems.

After completing this course, participants will be able to:

• Grasp how 6G will revolutionize edge computing and IoT architectures.
• Develop distributed systems for autonomous operations, high bandwidth, and ultra-low latency.
• Implement AI and data analytics at the edge to enable intelligent decision-making.
• Architect scalable, secure, and resilient edge infrastructures prepared for 6G.
• Assess the business and operational models facilitated by the convergence of 6G and edge computing.

Course Format

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

Course Customization Options

• For customized training arrangements, please contact us.

This instructor-led, live training in Norway (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 live, instructor-led training in Norway (online or onsite) is designed for intermediate developers, data scientists, and technology enthusiasts looking to gain practical skills in deploying AI models on edge devices for various applications.

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

• Grasp the core principles of Edge AI and its advantages.
• Establish and configure the necessary edge computing infrastructure.
• Create, train, and refine AI models tailored for edge deployment.
• Deploy practical AI solutions onto edge hardware.
• Assess and enhance the performance of models running on the edge.
• Navigate ethical and security aspects inherent to Edge AI applications.

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) is designed for advanced cybersecurity professionals, AI engineers, and IoT developers seeking to implement robust security measures and resilience strategies for Edge AI systems.

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

• Identifying security risks and vulnerabilities associated with Edge AI deployments.
• Applying encryption and authentication techniques to protect data.
• Architecting resilient Edge AI systems capable of withstanding cyber threats.
• Executing secure AI model deployment strategies within edge environments.

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.

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.

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.

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.

This instructor-led, live training in Norway (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 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, is tailored for intermediate-level professionals seeking to apply TinyML techniques to smart agriculture solutions that boost 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.

Course Customization Options

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