TinyML Training in Athens

This instructor-led, live training in Athens (online or onsite) is designed for intermediate-level embedded engineers, IoT developers, and AI researchers seeking to implement TinyML techniques for AI-driven 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.

This instructor-led training in Athens empowers advanced professionals with the competencies to design, optimize, and deploy complete TinyML pipelines. Through practical labs, participants will master data collection, low-power model training, and real-world application testing.

This instructor-led training in Athens empowers advanced professionals to safeguard TinyML pipelines on edge devices. You will gain the skills to deploy privacy-preserving techniques, reinforce models against adversarial threats, and implement best practices for secure data handling in constrained environments.

This live, instructor-led training in Athens guides advanced professionals in integrating TinyML into autonomous robotics. Participants will 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 Athens empowers intermediate professionals to implement TinyML for smart agriculture. It covers building lightweight models, integrating edge AI with IoT, and optimizing for precision irrigation and pest detection in a hands-on lab setting.

This instructor-led live training on Athens focuses on implementing TinyML solutions for healthcare monitoring and diagnostics. Participants will learn to design models for real-time health data processing, optimize for low-power wearables, and ensure clinical reliability. The course includes hands-on lab exercises.

This live, instructor-led training in Athens empowers advanced practitioners to refine TinyML models for use on resource-limited embedded devices. Learners will utilize quantization and pruning methods, construct low-latency inference pipelines, and assess performance within specific memory and energy boundaries.

This instructor-led, live training in Athens (online or onsite) is designed for 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 practical applications within IoT.
• Establish a TinyML development environment tailored for IoT projects.
• Create and deploy ML models onto low-power microcontrollers.
• Utilize TinyML to implement predictive maintenance and anomaly detection solutions.
• Optimize TinyML models to ensure efficient power consumption and memory utilization.

This instructor-led, live training in Athens (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 completing this training, 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, optimizing, and deploying AI models on low-power microcontrollers.
• Utilizing TensorFlow Lite and Edge Impulse to build practical TinyML solutions.
• Enhancing AI models for improved power efficiency and adherence to memory limitations.

This instructor-led, live training in Athens (online or onsite) is designed for engineers and data scientists at the beginner level who want to grasp the fundamentals of TinyML, explore its use cases, and deploy AI models on microcontrollers.

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

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