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

This instructor-led, live training in Serbia (online or onsite) is aimed at intermediate-level telecom professionals, AI engineers, and IoT specialists who wish to explore how 5G networks accelerate 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 21-hour training in Serbia empowers IT architects to design next-generation distributed systems. It explores the synergy of 6G, edge computing, and AI integration to build low-latency, scalable infrastructures. Participants gain practical skills to create secure, resilient, and intelligent edge architectures that meet evolving business demands.

This instructor-led, live training in Serbia (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 Serbia (conducted online or on-site) is designed for intermediate-level developers, data scientists, and tech enthusiasts aiming to develop practical skills in deploying AI models on edge devices for a variety of applications.

By the conclusion of this training, participants will be capable of:

• Understanding the fundamental principles of Edge AI and its key benefits.
• Setting up and configuring the edge computing infrastructure.
• Developing, training, and optimizing AI models for edge-specific deployment.
• Implementing functional AI solutions on edge devices.
• Evaluating and refining the performance of edge-deployed models.
• Managing ethical and security aspects in Edge AI applications.

This instructor-led session on Serbia empowers senior professionals to architect, optimize, and release full-scale TinyML pipelines. Through practical labs, learners will acquire the ability to gather data, train energy-efficient models, and validate real-world applications.

This instructor-led, live training in Serbia (online or onsite) is designed for advanced cybersecurity professionals, AI engineers, and IoT developers who wish to establish robust security protocols and resilience strategies for Edge AI systems.

Upon completing this training, participants will be capable of:

• Identifying security risks and vulnerabilities associated with Edge AI deployments.
• Deploying encryption and authentication methods to safeguard data.
• Architecting resilient Edge AI systems capable of withstanding cyber attacks.
• Executing secure AI model deployment strategies within edge environments.

This instructor-led, live training in Serbia (online or on-site) is designed for intermediate-level embedded systems engineers and AI developers aiming 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 principles 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.
• Leverage 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 Serbia enables advanced practitioners to optimize TinyML models for resource-constrained embedded devices. Participants will implement quantization and pruning techniques, construct low-latency inference pipelines, and benchmark performance against memory and energy constraints.

This instructor-led course in Serbia enables advanced professionals to fortify TinyML pipelines on edge devices. It covers the implementation of privacy-preserving strategies, hardening models against adversarial risks, and adhering to best practices for secure data management in constrained environments.

This guided, live training in Serbia (available online or in-person) is designed for beginner-level engineers and data scientists who want to understand the fundamentals of TinyML, explore its applications, and deploy AI models on microcontrollers.

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

• Grasping the core principles of TinyML and its importance.
• Implementing lightweight AI models on microcontrollers and edge devices.
• Optimizing and fine-tuning machine learning models to minimize power usage.
• Utilizing TinyML for practical scenarios including gesture recognition, anomaly detection, and audio processing.

This live, instructor-led training in Serbia 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 instructor-led live training in Serbia focuses on the practical implementation of TinyML for healthcare monitoring and diagnostics. Learners will master the design of models for real-time health data, optimization strategies for low-power wearables, and methods to guarantee clinical reliability. The curriculum includes hands-on lab exercises to reinforce theoretical concepts.

This instructor-led, live training in Serbia (online or onsite) is designed for 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, instructor-led course in Serbia enables intermediate professionals to implement TinyML in smart agriculture. Participants will learn to develop lightweight models, integrate edge AI with IoT, and optimize processes for precision irrigation and pest detection within a hands-on lab setting.