Edge AI Training in Plovdiv

This instructor-led, live training in Plovdiv (online or onsite) is designed for advanced AI researchers, data scientists, and security specialists interested in implementing federated learning techniques to train AI models across multiple edge devices while maintaining data privacy.

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

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

This instructor-led, live training in Plovdiv (online or onsite) targets beginner to intermediate-level agritech professionals, IoT specialists, and AI engineers who wish to develop and deploy 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 Plovdiv (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 Plovdiv (online or on-site) is designed for beginner to intermediate retail technologists, AI developers, and business analysts who wish to apply Edge AI solutions for smart checkout systems, inventory management, and personalized customer engagement.

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

• Understand how Edge AI enhances retail operations and customer experience.
• Implement AI-powered smart checkout and cashier-less payment systems.
• Optimize inventory management with real-time tracking and analytics.
• Utilize computer vision and AI for personalized in-store experiences.

This instructor-led live training in Plovdiv (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 session in Plovdiv (online or in-person) targets intermediate embedded AI developers and edge computing experts looking to refine and optimize compact AI models for deployment on devices with limited resources.

Upon completing 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 decrease model volume and latency.
• Refining models through transfer learning to enhance task-specific performance.
• Deploying optimized models on actual edge hardware platforms.

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

Upon completing this training, participants will be able to:

• Grasp 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.
• Utilize frameworks such as 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 Plovdiv (online or onsite) is designed for intermediate-level embedded engineers, IoT developers, and AI researchers who aim to implement TinyML techniques for AI-powered applications on energy-efficient hardware.

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

• Grasping the core principles of TinyML and edge AI.
• Implementing lightweight AI models on microcontrollers.
• Enhancing AI inference for minimal power usage.
• Incorporating TinyML into practical IoT solutions.

This instructor-led, live training in Plovdiv (online or onsite) is designed for robotics engineers, AI developers, and automation specialists at intermediate to advanced levels who aim to integrate Edge AI into robotics applications.

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

• Grasp the significance of Edge AI in autonomous systems.
• Deploy AI models on edge devices to support real-time robotics operations.
• Optimize AI performance to ensure low-latency decision-making.
• Combine computer vision and sensor fusion techniques for enhanced robotic autonomy.

Edge & Lightweight Agents is a hands-on course designed for deploying agentic AI workloads on devices with limited resources. Learners will acquire the skills to construct, optimize, and oversee lightweight agents capable of performing local reasoning and inference, thereby enhancing speed, privacy, and reliability within distributed systems. The curriculum highlights performance tuning, low-latency design strategies, and the integration of hardware and software.

This instructor-led, live training (available online or onsite) is tailored for intermediate-level professionals aiming to implement and optimize on-device agentic systems using Python and edge AI frameworks.

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

• Understanding the architecture and challenges associated with running agentic AI on edge devices.
• Designing lightweight agent loops that are suitable for constrained environments.
• Implementing local inference using TensorFlow Lite, PyTorch Mobile, and ONNX.
• Integrating agents with sensors, actuators, and IoT platforms.
• Optimizing performance, energy consumption, and latency for real-time operations.

Format of the Course

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

This instructor-led, live training in Plovdiv (online or onsite) targets advanced AI engineers, embedded developers, and hardware engineers looking to implement AI models on low-power devices while minimizing energy consumption.

Upon completing this training, participants will be capable of:

• Grasping the challenges associated with running AI on energy-efficient devices.
• Optimizing neural networks for low-power inference.
• Applying quantization, pruning, and model compression techniques.
• Deploying AI models on edge hardware with minimal power usage.

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

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

• Comprehend the challenges and requirements associated with deploying AI models on edge devices.
• Apply model compression techniques to decrease the size and complexity of AI models.
• Leverage quantization methods to boost model efficiency on edge hardware.
• Implement pruning and additional optimization techniques to enhance model performance.
• Deploy optimized AI models across various edge devices.

This instructor-led, live training conducted in Plovdiv (online or onsite) is tailored for intermediate-level developers, data scientists, and tech enthusiasts aiming 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:

• 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 Plovdiv (online or onsite) is tailored for intermediate-level finance professionals, fintech developers, and AI specialists who wish to implement Edge AI solutions in financial services.

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

• Grasp the significance of Edge AI in financial services.
• Build fraud detection systems using Edge AI.
• Improve customer service through AI-powered solutions.
• Utilize Edge AI for risk management and strategic decision-making.
• Deploy and oversee Edge AI solutions in financial settings.

This instructor-led, live training in Plovdiv (online or onsite) is tailored for intermediate-level industrial engineers, manufacturing professionals, and AI developers who wish to implement Edge AI solutions in 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 machines at the network's edge, facilitating real-time decision-making with minimal latency.

This instructor-led, live training (available online or onsite) is designed for advanced-level embedded and IoT professionals looking to deploy AI-driven logic and control systems in manufacturing settings where speed, reliability, and offline operation 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 devices.
• Utilize tools such as TensorFlow Lite and OpenVINO for low-latency inference.
• Integrate edge intelligence with sensors, actuators, and industrial protocols.

Course Format

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

Course Customization Options

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

This instructor-led, live training in Plovdiv (online or onsite) is aimed at intermediate-level developers, data scientists, and AI practitioners who wish to leverage TensorFlow Lite for Edge AI applications.

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

• Understand the fundamentals of TensorFlow Lite and its role in Edge AI.
• Develop and optimize AI models using TensorFlow Lite.
• Deploy TensorFlow Lite models on various edge devices.
• Utilize tools and techniques for model conversion and optimization.
• Implement practical Edge AI applications using TensorFlow Lite.

This instructor-led live training in Plovdiv (offered online or on-site) targets intermediate-level urban planners, civil engineers, and smart city project managers seeking to utilize Edge AI for smart city projects.

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

• Comprehend the role of Edge AI in smart city infrastructure.
• Deploy 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 Plovdiv (online or onsite) is aimed at intermediate-level cybersecurity professionals, system administrators, and AI ethics researchers who wish to secure and ethically deploy Edge AI solutions.

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

• Understand the security and privacy challenges in Edge AI.
• Implement best practices for securing edge devices and data.
• Develop strategies to mitigate security risks in Edge AI deployments.
• Address ethical considerations and ensure compliance with regulations.
• Conduct security assessments and audits for Edge AI applications.

This instructor-led, live training in Plovdiv (online or onsite) is aimed at 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 Plovdiv (online or onsite) is aimed at intermediate-level healthcare professionals, biomedical engineers, and AI developers who wish to leverage Edge AI for innovative healthcare solutions.

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

• Understand the role and benefits of Edge AI in healthcare.
• Develop and deploy AI models on edge devices for healthcare applications.
• Implement Edge AI solutions in wearable devices and diagnostic tools.
• Design and deploy patient monitoring systems using Edge AI.
• Address ethical and regulatory considerations in healthcare AI applications.

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

This instructor-led, live training (available online or onsite) targets intermediate-level embedded developers and robotics engineers aiming 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 fundamentals of TinyML and edge AI in robotics.
• Convert and deploy AI models for on-device inference.
• Optimize models for speed, size, and 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 with TinyML and edge AI toolchains.
• Practical exercises on embedded and robotic hardware platforms.

Course Customization Options

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

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

This instructor-led training, available online or onsite, targets intermediate-level IT architects seeking to grasp and design next-generation distributed architectures that leverage the synergy between 6G connectivity and intelligent edge systems.

Upon completing this course, participants will be able to:

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

Course Format

• 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 Plovdiv (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 facilitates robust AI inference on edge devices like the Ascend 310. This suite offers vital tools for compiling, optimizing, and deploying models in environments where computational power and memory are limited.

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

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

• Preparing and converting AI models for the Ascend 310 using CANN tools.
• Constructing lightweight inference pipelines with MindSpore Lite and AscendCL.
• Enhancing model performance within constrained compute and memory settings.
• Deploying and monitoring AI applications in real-world edge scenarios.

Course Format

• Interactive lectures and demonstrations.
• Practical lab exercises focusing on edge-specific models and scenarios.
• Live deployment examples on either virtual or physical edge hardware.

Course Customization Options

• For customized training options, please contact us to arrange.

This instructor-led, live training in Plovdiv (online or onsite) is aimed at intermediate-level developers, system architects, and industry professionals who wish to leverage Edge AI for enhancing IoT applications with intelligent data processing and analytics capabilities.

By the end 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 Plovdiv (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 instructor-led, live training in Plovdiv (online or onsite) is designed for intermediate-level developers and IT professionals who want to gain a comprehensive understanding of Edge AI, covering everything from conceptual foundations to practical implementation, including setup and deployment.

Upon completion of 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 Plovdiv (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.

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

This instructor-led live training, available online or on-site, is designed for intermediate-level developers looking to build and deploy AI models leveraging the BANGPy framework and Neuware SDK on Cambricon MLU hardware.

Upon completing this training, participants will be able to:

• Configure and set up the development environments for BANGPy and Neuware.
• Develop and optimize Python- and C++-based models for Cambricon MLUs.
• Deploy models to edge devices and data centers running the Neuware runtime.
• Integrate ML workflows with acceleration features specific to MLU hardware.

Course Format

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

Customization Options

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

This instructor-led, live training in Plovdiv (online or onsite) is designed for beginner-level developers and IT professionals who want to understand the fundamentals of Edge AI and its introductory applications.

Upon completing this training, participants will be able to:

• Grasp the basic concepts and architecture of Edge AI.
• Set up and configure Edge AI environments.
• Develop and deploy straightforward Edge AI applications.
• Identify and comprehend the use cases and benefits of Edge AI.