TinyML Training in Prague

This instructor-led, live training in Prague (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.

This instructor-guided training in Prague empowers senior professionals with the capabilities to architect, optimize, and release complete TinyML workflows. Learners will master data acquisition, low-power model training, and real-world application testing through hands-on laboratory exercises.

This guided training session on Prague empowers advanced professionals to secure TinyML pipelines on edge hardware. Learners will acquire the skills to apply privacy-preserving methods, reinforce models against adversarial threats, and adopt best practices for safe data processing in resource-limited contexts.

This instructor-led, live training in Prague equips advanced professionals with the skills to integrate TinyML into autonomous robotics. Participants will learn to design optimized models, implement on-device perception pipelines, and deploy lightweight AI on embedded hardware to achieve real-time autonomy.

This 21-hour instructor-led course in Prague empowers intermediate professionals to deploy TinyML for smart agriculture. Learn to build lightweight models, integrate edge AI with IoT, and optimize for precision irrigation and pest detection in a hands-on lab setting.

This live, instructor-led course in Prague focuses on deploying TinyML for healthcare monitoring and diagnostics. Learners will acquire the skills to design models for real-time health data, optimize them for low-power wearables, and guarantee clinical reliability, including hands-on lab work.

This live, instructor-led training in Prague prepares advanced practitioners to optimize TinyML models for resource-constrained embedded devices. Participants will learn to apply quantization and pruning, build low-latency inference pipelines, and evaluate performance against memory and energy limits.

This instructor-led, live training in Prague (online or onsite) targets intermediate-level IoT developers, embedded engineers, and AI practitioners aiming to implement TinyML for predictive maintenance, anomaly detection, and smart sensor applications.

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

• Grasp the core concepts of TinyML and its role in IoT.
• Establish a TinyML development environment for IoT initiatives.
• Create and deploy machine learning models on low-power microcontrollers.
• Apply TinyML for predictive maintenance and anomaly detection.
• Optimize TinyML models to maximize power efficiency and minimize memory usage.

This instructor-led, live training in Prague (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.

This instructor-led, live training in Prague (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.