TinyML Training in Copenhagen

This instructor-led live training conducted in Copenhagen (online or onsite) targets intermediate-level embedded engineers, IoT developers, and AI researchers aiming to leverage TinyML methods for AI-driven applications on energy-efficient hardware.

Upon completion, participants will be equipped to:

• Grasp the core concepts of TinyML and edge AI.
• Implement lightweight AI models on microcontrollers.
• Refine AI inference processes to minimize power usage.
• Merge TinyML capabilities into practical IoT solutions.

This instructor-led training in Copenhagen empowers advanced professionals to design, optimize, and deploy full-scale TinyML pipelines. Participants will gain the ability to collect data, train low-power models, and validate real-world applications through practical lab work.

This instructor-led training in Copenhagen enables advanced professionals to safeguard TinyML pipelines on edge devices. You will learn to implement privacy-preserving techniques, harden models against adversarial threats, and apply best practices for secure data handling in constrained environments.

This live, instructor-led training in Copenhagen guides advanced professionals in integrating TinyML into autonomous robotics. Attendees will learn to design optimized models, construct on-device perception pipelines, and implement lightweight AI on embedded hardware to achieve real-time autonomy.

This 21-hour instructor-led course in Copenhagen enables intermediate professionals to implement TinyML for smart agriculture. Participants will learn to create lightweight models, integrate edge AI with IoT systems, and optimize solutions for precision irrigation and pest detection within a hands-on lab environment.

This live, instructor-led training in Copenhagen explores the implementation of TinyML for healthcare monitoring and diagnostics. Learners will master the design of models for real-time health data, optimization for low-power wearables, and strategies for ensuring clinical reliability. The curriculum includes hands-on laboratory exercises.

This live, instructor-led course in Copenhagen prepares advanced professionals to optimize TinyML models for resource-limited embedded devices. Learners will apply quantization and pruning, build low-latency inference pipelines, and evaluate performance against memory and energy constraints.

This live, instructor-guided training in Copenhagen (online or onsite) is crafted for intermediate IoT developers, embedded engineers, and AI practitioners seeking to implement TinyML for predictive maintenance, anomaly detection, and smart sensor functionalities.

By the end of the session, participants will be able to:

• Understand the basics of TinyML and its IoT applications.
• Establish a TinyML development setup for IoT projects.
• Create and implement ML models on low-power microcontrollers.
• Use TinyML to build predictive maintenance and anomaly detection features.
• Refine TinyML models for optimal power and memory performance.

This instructor-led live training, delivered in Copenhagen (online or onsite), is tailored for intermediate-level embedded systems engineers and AI developers aiming to implement machine learning models on microcontrollers using TensorFlow Lite and Edge Impulse.

Upon completion, participants will be capable of:

• Grasping the core principles of TinyML and its advantages for edge AI applications.
• Configuring a development environment tailored for TinyML projects.
• Training, refining, and implementing AI models on low-power microcontrollers.
• Leveraging TensorFlow Lite and Edge Impulse to build practical TinyML solutions.
• Optimizing AI models to meet power efficiency and memory limitations.

Aimed at novice engineers and data scientists, this instructor-led live session in Copenhagen (either online or on-site) provides a comprehensive look at TinyML basics, its various applications, and the deployment of AI models on microcontrollers.

By the time the session concludes, participants will be capable of:

• Understanding the core tenets of TinyML and its relevance.
• Executing lightweight AI models on microcontrollers and edge devices.
• Refining and tuning machine learning models to ensure low power consumption.
• Applying TinyML to real-world scenarios including gesture detection, anomaly identification, and audio analysis.