TinyML Training in Plovdiv

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

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

• Grasping the core principles of TinyML and edge AI.
• Implementing lightweight AI models on microcontrollers.
• Enhancing AI inference for minimal power usage.
• Incorporating TinyML into practical IoT solutions.

This instructor-led program in Plovdiv provides advanced professionals with the expertise to design, refine, and implement complete TinyML workflows. Through practical labs, learners will master data collection, the training of low-power models, and the validation of real-world applications.

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

This instructor-led, live training in Plovdiv guides advanced professionals in integrating 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 live course in Plovdiv equips mid-level professionals to implement TinyML in smart agriculture. Participants will learn to create lightweight models, link edge AI with IoT, and refine systems for precision irrigation and pest detection in a hands-on lab environment.

This live, instructor-led training in Plovdiv focuses on the implementation of TinyML solutions for healthcare monitoring and diagnostics. Participants will learn to design models for real-time health data processing, optimize them for low-power wearable devices, and verify clinical reliability. The course includes comprehensive hands-on lab exercises.

This live, instructor-led session in Plovdiv empowers advanced practitioners to optimize TinyML models for embedded devices operating under strict resource constraints. Learners will apply quantization and pruning techniques, build low-latency inference pipelines, and evaluate performance against memory and energy boundaries.

This instructor-led, live training in Plovdiv (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.

This instructor-led, live training in Plovdiv (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 completion of this training, participants will be able to:

• Grasp the fundamentals of TinyML and its advantages for edge AI applications.
• Configure 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.
• Enhance AI models for better power efficiency and memory utilization.

This instructor-led, live training in Plovdiv (online or onsite) is designed for beginner-level engineers and data scientists who want to understand TinyML fundamentals, explore its applications, and deploy AI models on microcontrollers.

Upon completing this training, participants will be able to:

• Understand the fundamentals of TinyML and its significance.
• Deploy lightweight AI models on microcontrollers and edge devices.
• Optimize and fine-tune machine learning models for low-power consumption.
• Apply TinyML for real-world applications such as gesture recognition, anomaly detection, and audio processing.