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

This instructor-led, live training in Turkey (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 instructor-led live training in Turkey (online or onsite) targets 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.

Upon completing this training, participants will be capable of:

• Exploring advanced techniques in Edge AI model development and optimization.
• Implementing cutting-edge strategies for deploying AI models on edge devices.
• Utilizing specialized tools and frameworks for advanced Edge AI applications.
• Optimizing the performance and efficiency of Edge AI solutions.
• Investigating innovative use cases and emerging trends in Edge AI.
• Addressing advanced ethical and security considerations in Edge AI deployments.

This instructor-led, live training in Turkey (available online or on-site) is tailored for intermediate-level developers, data scientists, and technology enthusiasts who aim to acquire practical skills in deploying AI models on edge devices for diverse applications.

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

• Grasp the foundational principles of Edge AI and its benefits.
• Configure and set up edge computing environments.
• Develop, train, and optimize AI models for edge deployment.
• Implement practical AI solutions on edge devices.
• Monitor and improve the performance of edge-deployed models.
• Handle ethical and security considerations in Edge AI applications.

This instructor-led course in Turkey empowers advanced professionals to architect, optimize, and launch complete TinyML pipelines. Learners will acquire the ability to acquire data, train energy-efficient models, and validate real-world applications through hands-on lab work.

This instructor-led, live training in Turkey (online or onsite) is designed for advanced-level cybersecurity professionals, AI engineers, and IoT developers who aim to implement robust security measures and resilience strategies for Edge AI systems.

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

• Comprehend the security risks and vulnerabilities associated with Edge AI deployments.
• Deploy encryption and authentication techniques to ensure data protection.
• Architect resilient Edge AI systems capable of withstanding cyber threats.
• Execute secure AI model deployment strategies within edge environments.

This instructor-led, live training in Turkey (online or onsite) targets intermediate-level embedded systems engineers and AI developers who aim to deploy machine learning models on microcontrollers using TensorFlow Lite and Edge Impulse.

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

• Grasp the core principles of TinyML and its advantages for edge AI applications.
• Configure a development environment tailored for TinyML projects.
• Train, optimize, and deploy AI models on low-power microcontrollers.
• Leverage TensorFlow Lite and Edge Impulse to build practical TinyML solutions.
• Enhance AI models for power efficiency and address memory limitations.

This live, instructor-led training in Turkey empowers advanced professionals to optimize TinyML models for resource-constrained embedded environments. Learners will apply quantization and pruning techniques, develop low-latency inference pipelines, and evaluate performance against strict memory and energy constraints.

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

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

By the end of 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 Turkey guides advanced professionals in integrating TinyML into autonomous robotics. Learners will design optimized models, implement on-device perception pipelines, and deploy lightweight AI on embedded hardware to achieve real-time autonomy.

This instructor-led live session in Turkey focuses on implementing TinyML solutions for healthcare monitoring and diagnostics. Participants will learn to design models for real-time health data processing, optimize them for low-power wearables, and ensure clinical reliability, supported by hands-on lab exercises.

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

By the conclusion 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 Turkey empowers intermediate professionals to deploy TinyML for smart agriculture. Participants will learn to build lightweight models, integrate edge AI with IoT, and optimize for precision irrigation and pest detection in a hands-on lab setting.