Edge AI Training in Belgrade

This instructor-led live training at Belgrade (online or onsite) targets advanced-level AI researchers, data scientists, and security specialists who seek to implement federated learning techniques for training AI models across multiple edge devices while preserving data privacy.

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

• Understand the principles and benefits of federated learning in Edge AI.
• Implement federated learning models using TensorFlow Federated and PyTorch.
• Optimize AI training across distributed edge devices.
• Address data privacy and security challenges in federated learning.
• Deploy and monitor federated learning systems in real-world applications.

This instructor-led live training, conducted in Belgrade (either online or on-site), targets beginner to intermediate-level agritech professionals, IoT specialists, and AI engineers interested in developing and deploying Edge AI solutions for smart farming.

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

• Understand the role of Edge AI in precision agriculture.
• Implement AI-driven crop and livestock monitoring systems.
• Develop automated irrigation and environmental sensing solutions.
• Optimize agricultural efficiency using real-time Edge AI analytics.

This instructor-led, live training in Belgrade (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 Belgrade (online or onsite) is designed for retail technologists, AI developers, and business analysts with beginner to intermediate proficiency who aim to leverage Edge AI solutions for smart checkout systems, inventory control, and personalized customer engagement.

Upon completion of this training, participants will be able to:

• Comprehend how Edge AI enhances retail operations and customer interactions.
• Deploy AI-driven smart checkout and cashier-less payment systems.
• Improve inventory management through real-time tracking and analytics.
• Apply computer vision and AI to create personalized in-store experiences.

This instructor-led, live training in Belgrade (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 Belgrade (online or onsite) is designed for intermediate-level embedded AI developers and edge computing specialists aiming to fine-tune and optimize lightweight AI models for deployment on resource-constrained devices.

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

• Identifying and adapting pre-trained models appropriate for edge deployment.
• Utilizing quantization, pruning, and other compression methods to minimize model size and reduce latency.
• Fine-tuning models through transfer learning to achieve task-specific performance.
• Deploying optimized models on actual edge hardware platforms.

This instructor-led, live training in Belgrade (online or onsite) is aimed at intermediate-level to advanced-level computer vision engineers, AI developers, and IoT professionals who wish to implement and optimize computer vision models for real-time processing on edge devices.

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

• Understand the fundamentals of Edge AI and its applications in computer vision.
• Deploy optimized deep learning models on edge devices for real-time image and video analysis.
• Use frameworks like TensorFlow Lite, OpenVINO, and NVIDIA Jetson SDK for model deployment.
• Optimize AI models for performance, power efficiency, and low-latency inference.

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

Upon completion of this training, participants will be able to:

• Grasp the core concepts of TinyML and edge AI.
• Deploy lightweight AI models on microcontrollers.
• Optimize AI inference processes for minimal power consumption.
• Integrate TinyML solutions into practical IoT applications.

This instructor-led, live training in Belgrade (online or on-site) is designed for intermediate to advanced robotics engineers, AI developers, and automation specialists who want to implement Edge AI in robotics applications.

Upon completion of this training, participants will be able to:

• Comprehend the role of Edge AI in autonomous systems.
• Deploy AI models on edge devices for real-time robotics operations.
• Optimize AI performance for low-latency decision-making.
• Integrate computer vision and sensor fusion for robotic autonomy.

The 'Edge & Lightweight Agents' course offers a hands-on approach to deploying agentic AI workloads on devices with limited resources. Participants will learn how to construct, optimize, and oversee lightweight agents that perform local reasoning and inference, thereby enhancing speed, privacy, and reliability in distributed systems. The curriculum places strong emphasis on performance tuning, low-latency design, and the integration of hardware with software.

This instructor-led, live training is available both online and onsite. It is designed for intermediate-level professionals looking to implement and optimize on-device agentic systems using Python and edge AI frameworks.

Upon completion of this training, participants will be able to:

• Grasp the architecture and challenges associated with running agentic AI on edge devices.
• Design lightweight agent loops that are suitable for constrained environments.
• Implement local inference using TensorFlow Lite, PyTorch Mobile, and ONNX.
• Integrate agents with sensors, actuators, and IoT platforms.
• Optimize performance, energy consumption, and latency for real-time operation.

Course Format

• Interactive lectures combined with practical demonstrations.
• Hands-on development within local or emulated environments.
• Project-based learning supported by guided implementation exercises.

Course Customization Options

• To request customized training for this course, please contact us to arrange it.

This instructor-led, live training in Belgrade (online or onsite) is designed for advanced AI engineers, embedded developers, and hardware engineers who wish to implement AI models on low-power devices while minimizing energy consumption.

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

• Understand the challenges of running AI on energy-efficient devices.
• Optimize neural networks for low-power inference.
• Utilize quantization, pruning, and model compression techniques.
• Deploy AI models on edge hardware with minimal power usage.

This instructor-led, live training in Belgrade (online or onsite) is aimed at intermediate-level AI developers, embedded engineers, and robotics engineers who wish to optimize and deploy AI models on NVIDIA Jetson platforms for edge applications.

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

• Understand the fundamentals of edge AI and NVIDIA Jetson hardware.
• Optimize AI models for deployment on edge devices.
• Use TensorRT for accelerating deep learning inference.
• Deploy AI models using JetPack SDK and ONNX Runtime.

This instructor-led, live training in Belgrade (online or onsite) is tailored for intermediate-level AI developers, machine learning engineers, and system architects aiming to optimize AI models for edge deployment.

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

• Comprehending the obstacles and specifications associated with deploying AI models on edge devices.
• Employing model compression techniques to decrease the size and complexity of AI models.
• Leveraging quantization approaches to boost model efficiency on edge hardware.
• Executing pruning and other optimization strategies to elevate model performance.
• Deploying refined AI models across various edge devices.

This instructor-led, live training in Belgrade (online or onsite) is aimed at intermediate-level developers, data scientists, and tech enthusiasts who wish to gain practical skills in deploying AI models on edge devices for various applications.

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

• Understand the principles of Edge AI and its benefits.
• 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.

This instructor-led, live training in Belgrade (online or onsite) is aimed at intermediate-level finance professionals, fintech developers, and AI specialists who wish to implement Edge AI solutions in financial services.

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

• Understand the role of Edge AI in financial services.
• Implement fraud detection systems using Edge AI.
• Enhance customer service through AI-driven solutions.
• Apply Edge AI for risk management and decision-making.
• Deploy and manage Edge AI solutions in financial environments.

This instructor-led live training in Belgrade (online or on-site) is designed for intermediate-level industrial engineers, manufacturing professionals, and AI developers interested in implementing Edge AI solutions for industrial automation.

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

• Understand the role of Edge AI in industrial automation.
• Implement predictive maintenance solutions using Edge AI.
• Apply AI techniques for quality control in manufacturing processes.
• Optimize industrial processes using Edge AI.
• Deploy and manage Edge AI solutions in industrial environments.

Edge AI involves deploying artificial intelligence models directly onto devices and machinery at the network's edge, facilitating real-time decision-making with minimal latency.

This instructor-led live training, available online or on-site, targets advanced embedded and IoT professionals seeking to implement AI-driven logic and control systems in manufacturing settings where speed, reliability, and offline capability are paramount.

Upon completion of this training, participants will be able to:

• Grasp the architecture and advantages of edge AI systems.
• Construct and optimize AI models for deployment on embedded hardware.
• Utilize tools such as TensorFlow Lite and OpenVINO for low-latency inference.
• Integrate edge intelligence with sensors, actuators, and industrial communication protocols.

Course Format

• Interactive lectures and discussions.
• Extensive exercises and practical sessions.
• Hands-on implementation within a live-lab environment.

Customization Options

• To request a customized version of this course, please contact us to arrange.

This instructor-led, live course in Belgrade (online or onsite) targets intermediate developers, data scientists, and AI professionals aiming to utilize TensorFlow Lite for Edge AI initiatives.

Upon completion of this training, participants will be able to:

• Comprehend the foundational aspects of TensorFlow Lite and its role in Edge AI.
• Build and optimize AI models using TensorFlow Lite.
• Deploy TensorFlow Lite models on diverse edge devices.
• Employ tools and techniques for model conversion and optimization.
• Develop practical Edge AI applications using TensorFlow Lite.

This instructor-led, live training in Belgrade (online or on-site) is designed for intermediate-level urban planners, civil engineers, and smart city project managers aiming to leverage Edge AI for urban initiatives.

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

• Understand the role of Edge AI in smart city infrastructures.
• Implement Edge AI solutions for traffic management and surveillance.
• Optimize urban resources using Edge AI technologies.
• Integrate Edge AI with existing smart city systems.
• Address ethical and regulatory considerations in smart city deployments.

This instructor-led, live training in Belgrade (online or onsite) targets intermediate-level cybersecurity professionals, system administrators, and AI ethics researchers aiming to secure and ethically deploy Edge AI solutions.

Upon completing this training, participants will be able to:

• Grasp the security and privacy challenges inherent to Edge AI.
• Apply best practices for securing edge devices and data.
• Formulate strategies to mitigate security risks in Edge AI deployments.
• Tackle ethical considerations and ensure adherence to regulations.
• Perform security assessments and audits for Edge AI applications.

This instructor-led, live training in Belgrade (available online or onsite) is designed for intermediate-level robotics engineers, autonomous vehicle developers, and AI researchers who wish to leverage Edge AI for innovative autonomous system solutions.

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

• Understand the role and benefits of Edge AI in autonomous systems.
• Develop and deploy AI models for real-time processing on edge devices.
• Implement Edge AI solutions in autonomous vehicles, drones, and robotics.
• Design and optimize control systems using Edge AI.
• Address ethical and regulatory considerations in autonomous AI applications.

This instructor-led, live training in Belgrade (online or in-person) is designed for intermediate-level healthcare practitioners, biomedical engineers, and AI developers aiming to utilize Edge AI for innovative healthcare solutions.

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

• Understanding the role and benefits of Edge AI in healthcare.
• Developing and deploying AI models on edge devices for healthcare applications.
• Implementing Edge AI solutions in wearable devices and diagnostic tools.
• Designing and deploying patient monitoring systems using Edge AI.
• Addressing ethical and regulatory considerations in healthcare AI applications.

Edge AI allows artificial intelligence models to operate directly on embedded or resource-limited devices, which lowers latency and power usage while boosting autonomy and privacy within robotic systems.

This instructor-led live training (available online or onsite) targets intermediate embedded developers and robotics engineers who want to implement machine learning inference and optimization techniques directly on robotic hardware using TinyML and edge AI frameworks.

Upon completing this training, participants will be able to:

• Grasp the core principles of TinyML and edge AI for robotics.
• Convert and deploy AI models for on-device inference.
• Optimize models to enhance speed, reduce size, and improve energy efficiency.
• Integrate edge AI systems into robotic control architectures.
• Evaluate performance and accuracy in real-world scenarios.

Course Format

• Interactive lectures and discussions.
• Hands-on practice utilizing TinyML and edge AI toolchains.
• Practical exercises performed on embedded and robotic hardware platforms.

Course Customization Options

• To request customized training for this course, please contact us to make arrangements.

The course '6G and the Intelligent Edge' offers a forward-looking perspective on integrating 6G wireless technologies with edge computing, IoT ecosystems, and AI-driven data processing to build intelligent, low-latency, and adaptive infrastructures.

This instructor-led, live training (available online or onsite) is designed for intermediate-level IT architects who aim to comprehend and design next-generation distributed architectures that leverage the synergy of 6G connectivity and intelligent edge systems.

Upon completing this course, participants will be able to:

• Grasp how 6G will transform edge computing and IoT architectures.
• Design distributed systems capable of ultra-low latency, high bandwidth, and autonomous operations.
• Integrate AI and data analytics at the edge to enable intelligent decision-making.
• Plan scalable, secure, and resilient 6G-ready edge infrastructures.
• Evaluate the business and operational models enabled by the convergence of 6G and edge technologies.

Format of the Course

• Interactive lectures and discussions.
• Case studies and applied architecture design exercises.
• Hands-on simulation with optional edge or container tools.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

This instructor-led, live training in Belgrade (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.

Huawei's Ascend CANN toolkit empowers powerful AI inference on edge devices like the Ascend 310. CANN offers essential tools for compiling, optimizing, and deploying models in environments with limited compute and memory resources.

This instructor-led, live training (online or onsite) targets intermediate-level AI developers and integrators who want to deploy and optimize models on Ascend edge devices using the CANN toolchain.

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

• Prepare and convert AI models for the Ascend 310 using CANN tools.
• Build lightweight inference pipelines using MindSpore Lite and AscendCL.
• Optimize model performance for environments with limited compute and memory.
• Deploy and monitor AI applications in real-world edge use cases.

Format of the Course

• Interactive lecture and demonstration.
• Hands-on lab work with edge-specific models and scenarios.
• Live deployment examples on virtual or physical edge hardware.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

This instructor-led live training in Belgrade (online or onsite) targets intermediate-level developers, system architects, and industry professionals eager to leverage Edge AI to boost IoT applications with intelligent data processing and analytics.

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

• Understand the fundamentals of Edge AI and its application in IoT.
• Set up and configure Edge AI environments for IoT devices.
• Develop and deploy AI models on edge devices for IoT applications.
• Implement real-time data processing and decision-making in IoT systems.
• Integrate Edge AI with various IoT protocols and platforms.
• Address ethical considerations and best practices in Edge AI for IoT.

This instructor-led, live training in Belgrade (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 instructor-led, live training in Belgrade (online or onsite) is designed for intermediate-level developers and IT professionals seeking a comprehensive understanding of Edge AI, from initial concepts to practical implementation, including setup and deployment.

Upon completing this training, participants will be able to:

• Grasp the fundamental concepts of Edge AI.
• Set up and configure Edge AI environments.
• Develop, train, and optimize Edge AI models.
• Deploy and manage Edge AI applications.
• Integrate Edge AI with existing systems and workflows.
• Address ethical considerations and best practices in Edge AI implementation.

This instructor-led, live training in Belgrade (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.

Cambricon MLUs (Machine Learning Units) are specialized AI chips designed for efficient inference and training in both edge computing and data center environments.

This instructor-led live training (available online or on-site) targets intermediate developers looking to build and deploy AI models utilizing the BANGPy framework and Neuware SDK on Cambricon MLU hardware.

Upon completing this training, participants will be equipped to:

• Configure and set up the BANGPy and Neuware development environments.
• Develop and optimize Python- and C++-based models for Cambricon MLUs.
• Deploy models to edge and data center devices operating on the Neuware runtime.
• Integrate machine learning workflows with MLU-specific acceleration capabilities.

Course Format

• Interactive lectures and discussions.
• Practical, hands-on experience with BANGPy and Neuware for development and deployment.
• Guided exercises focusing on optimization, integration, and testing.

Customization Options

• For customized training tailored to your specific Cambricon device model or use case, please contact us to arrange.

This instructor-led live training in Belgrade (online or onsite) is aimed at beginner-level developers and IT professionals who wish to understand the fundamentals of Edge AI and its introductory applications.

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

• Understand the basic concepts and architecture of Edge AI.
• Set up and configure Edge AI environments.
• Develop and deploy simple Edge AI applications.
• Identify and understand the use cases and benefits of Edge AI.

Combine the transformative power of AI with the agility of edge computing in this comprehensive course. Learn to deploy AI models directly on edge devices, from understanding CNN architectures to mastering knowledge distillation and federated learning. This hands-on training will equip you with the skills to optimize AI performance for real-time processing and decision-making at the edge.