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

This instructor-led live training in Bulgaria (online or onsite) targets intermediate-level telecom professionals, AI engineers, and IoT specialists interested in exploring the role of 5G networks in accelerating Edge AI applications.

By the end of this training, participants will be able to:

• Understand the fundamentals of 5G technology and its impact on Edge AI.
• Deploy AI models optimized for low-latency applications in 5G environments.
• Implement real-time decision-making systems using Edge AI and 5G connectivity.
• Optimize AI workloads for efficient performance on edge devices.

This instructor-led, live training in Bulgaria (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 Bulgaria (delivered online or on-site) is designed for intermediate developers, data scientists, and tech enthusiasts looking to acquire hands-on expertise in deploying AI models on edge devices for a variety of applications.

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

• Understanding the fundamental principles of Edge AI and its key benefits.
• Setting up and configuring an edge computing environment.
• Developing, training, and optimizing AI models for edge deployment.
• Implementing practical AI solutions on edge hardware.
• Evaluating and enhancing the performance of edge-deployed models.
• Addressing ethical and security implications in Edge AI applications.

This instructor-led program in Bulgaria 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, live training in Bulgaria (online or onsite) targets advanced cybersecurity professionals, AI engineers, and IoT developers who wish to implement robust security measures and resilience strategies for Edge AI systems.

By the end of this training, participants will be able to:

• Grasp the security risks and vulnerabilities associated with Edge AI deployments.
• Apply encryption and authentication techniques to protect data.
• Design resilient Edge AI architectures capable of withstanding cyber threats.
• Utilize secure strategies for deploying AI models in edge environments.

This instructor-led, live training in Bulgaria (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 live, instructor-led session in Bulgaria 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 training on Bulgaria 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 Bulgaria (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.

This instructor-led, live training in Bulgaria 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 live, instructor-led training in Bulgaria 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 instructor-led, live training in Bulgaria (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 21-hour live course in Bulgaria 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.