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 forward-looking course examines the convergence of 6G wireless technologies, edge computing, IoT ecosystems, and AI-driven data processing to build intelligent, adaptive, and low-latency infrastructures.

Designed for intermediate IT architects, this instructor-led training (available online or onsite) focuses on understanding and designing next-generation distributed architectures that leverage the combined power of 6G connectivity and intelligent edge systems.

After completing this course, participants will be able to:

• Comprehend how 6G will reshape edge computing and IoT architectures.
• Architect distributed systems optimized for ultra-low latency, high bandwidth, and autonomous operations.
• Implement AI and data analytics at the edge to enable intelligent decision-making.
• Plan scalable, secure, and resilient infrastructures ready for 6G adoption.
• Assess the business and operational models facilitated by the 6G-edge convergence.

Course Format

• Interactive lectures and collaborative discussions.
• Case studies and practical architecture design exercises.
• Hands-on simulations using optional edge or container-based tools.

Customization Options

• To arrange customized training for this course, please contact us to discuss your specific needs.

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 (offered online or onsite) is tailored for intermediate-level developers, data scientists, and tech enthusiasts seeking practical expertise in deploying AI models on edge devices for diverse applications.

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

• Understand the fundamental principles and benefits of Edge AI.
• Set up and configure the edge computing environment.
• Develop, train, and optimize AI models for edge deployment.
• Implement practical AI solutions on edge devices.
• Evaluate and improve the performance of edge-deployed models.
• Address ethical and security considerations in Edge AI applications.

TinyML involves the deployment of optimized machine learning models on edge devices with limited resources.

This live training, led by an instructor and available online or in-person, is designed for advanced technical professionals aiming to design, optimize, and deploy comprehensive TinyML pipelines.

Upon completing this training, participants will be able to:

• Gather, prepare, and manage datasets for TinyML solutions.
• Train and optimize models for low-power microcontrollers.
• Transform models into lightweight formats appropriate for edge devices.
• Deploy, test, and monitor TinyML applications on actual hardware.

Course Format

• Instructor-led lectures accompanied by technical discussions.
• Practical labs and iterative experimentation.
• Hands-on deployment on microcontroller-based platforms.

Customization Options

• To tailor the training to specific toolchains, hardware boards, or internal workflows, please contact us to make arrangements.

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.

TinyML involves the deployment of machine learning models on hardware with strict resource limitations.

This instructor-led training session, available both online and onsite, targets advanced practitioners seeking to optimize TinyML models for deployment on embedded devices, focusing on low latency and memory efficiency.

Upon successful completion of this training, participants will be equipped to:

• Utilize quantization, pruning, and compression strategies to minimize model size while maintaining accuracy.
• Benchmark TinyML models to assess latency, memory usage, and energy efficiency.
• Implement optimized inference pipelines on microcontrollers and edge devices.
• Assess the trade-offs between performance, accuracy, and hardware limitations.

Course Format

• Instructional presentations complemented by technical demonstrations.
• Practical exercises in optimization and comparative performance testing.
• Hands-on implementation of TinyML pipelines within a controlled lab setting.

Customization Options

• To tailor the training to specific hardware platforms or internal workflows, please reach out to us for program customization.

TinyML involves deploying machine learning models onto low-power, resource-constrained devices that operate at the network edge.

This instructor-led live training, available online or onsite, is designed for advanced professionals aiming to secure TinyML pipelines and implement privacy-preserving techniques in edge AI applications.

Upon completing this course, participants will be able to:

• Recognize security risks specific to on-device TinyML inference.
• Deploy privacy-preserving mechanisms for edge AI deployments.
• Harden TinyML models and embedded systems against adversarial threats.
• Apply best practices for secure data handling in constrained environments.

Format of the Course

• Engaging lectures supported by expert-led discussions.
• Practical exercises emphasizing real-world threat scenarios.
• Hands-on implementation using embedded security and TinyML tooling.

Course Customization Options

• Organizations may request a tailored version of this training to align with their specific security and compliance needs.

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.

TinyML serves as a framework for deploying machine learning models onto low-power microcontrollers and embedded platforms utilized in robotics and autonomous systems.

This instructor-led, live training (available online or onsite) targets advanced-level professionals seeking to integrate TinyML-based perception and decision-making capabilities into autonomous robots, drones, and intelligent control systems.

Upon completing this course, participants will be able to:

• Design optimized TinyML models tailored for robotics applications.
• Implement on-device perception pipelines to achieve real-time autonomy.
• Integrate TinyML solutions into existing robotic control frameworks.
• Deploy and test lightweight AI models on embedded hardware platforms.

Course Format

• Technical lectures combined with interactive discussions.
• Hands-on labs focusing on embedded robotics tasks.
• Practical exercises simulating real-world autonomous workflows.

Course Customization Options

• For organization-specific robotics environments, customization can be arranged upon request.

TinyML involves embedding machine learning capabilities into low-power, resource-constrained wearable and medical devices.

This instructor-led live training, available online or onsite, targets intermediate-level practitioners seeking to implement TinyML solutions for healthcare monitoring and diagnostic applications.

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

• Design and deploy TinyML models for real-time health data processing.
• Collect, preprocess, and interpret biosensor data to derive AI-driven insights.
• Optimize models for low-power and memory-constrained wearable devices.
• Assess the clinical relevance, reliability, and safety of TinyML-driven outputs.

Course Format

• Lectures complemented by live demonstrations and interactive discussions.
• Hands-on practice using wearable device data and TinyML frameworks.
• Implementation exercises conducted within a guided lab environment.

Customization Options

• For tailored training that aligns with specific healthcare devices or regulatory workflows, please contact us to customize the program.

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.

TinyML is a machine learning approach optimized for small, resource-constrained devices.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level learners who wish to build working TinyML applications using Raspberry Pi, Arduino, and similar microcontrollers.

Upon completing this training, attendees will gain the skills to:

• Collect and prepare data for TinyML projects.
• Train and optimize small machine learning models for microcontroller environments.
• Deploy TinyML models on Raspberry Pi, Arduino, and related boards.
• Develop end-to-end embedded AI prototypes.

Format of the Course

• Instructor-led presentations and guided discussions.
• Practical exercises and hands-on experimentation.
• Live-lab project work on real hardware.

Course Customization Options

• For tailored training aligned with your specific hardware or use case, please contact us to arrange.

TinyML enables the deployment of machine learning models on low-power, resource-constrained devices directly in the field.

This instructor-led live training, available online or onsite, is tailored for intermediate professionals seeking to apply TinyML techniques to smart agriculture solutions that boost automation and environmental intelligence.

Upon completing this program, participants will be able to:

• Develop and deploy TinyML models for agricultural sensing applications.
• Integrate edge AI into IoT ecosystems for automated crop monitoring.
• Leverage specialized tools to train and optimize lightweight models.
• Establish workflows for precision irrigation, pest detection, and environmental analytics.

Course Format

• Guided presentations coupled with applied technical discussions.
• Hands-on practice using real-world datasets and devices.
• Practical experimentation within a supported lab environment.

Course Customization Options

• For customized training aligned with specific agricultural systems, please contact us to tailor the program.