Edge AI Training in Tashkent

This instructor-led, live training in Tashkent (available online or on-site) is designed for advanced-level AI researchers, data scientists, and security specialists who aim to implement federated learning techniques for training AI models across multiple edge devices while maintaining data privacy.

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

• Comprehend the principles and advantages of federated learning within Edge AI.
• Implement federated learning models using TensorFlow Federated and PyTorch.
• Optimise AI training across distributed edge devices.
• Tackle 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 Tashkent (online or onsite) is aimed at beginner-level 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 Tashkent (online or on-site) is aimed at advanced-level 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:

• Understand security risks and vulnerabilities in Edge AI deployments.
• Implement encryption and authentication techniques for data protection.
• Design resilient Edge AI architectures that can withstand cyber threats.
• Apply secure AI model deployment strategies in edge environments.

This instructor-led, live training in Tashkent (online or on-site) is designed for beginner to intermediate-level retail technologists, AI developers, and business analysts who wish to apply Edge AI solutions for smart checkout systems, inventory management, and personalized customer engagement.

By the end 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 Tashkent (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 Tashkent (online or on-site) is designed for intermediate-level embedded AI developers and edge computing specialists seeking to fine-tune and optimise lightweight AI models for deployment on resource-constrained devices.

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

• Select and adapt pre-trained models suitable for edge deployment.
• Apply quantisation, pruning, and other compression techniques to reduce model size and latency.
• Fine-tune models using transfer learning to achieve task-specific performance.
• Deploy optimised models on real-world edge hardware platforms.

This instructor-led, live training in Tashkent (online or on-site) is designed for intermediate to advanced-level computer vision engineers, AI developers, and IoT professionals who aim to implement and optimise 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 optimised deep learning models on edge devices for real-time image and video analysis.
• Utilise frameworks such as TensorFlow Lite, OpenVINO, and NVIDIA Jetson SDK for model deployment.
• Optimise AI models for performance, power efficiency, and low-latency inference.

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

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

• Understand the fundamentals of TinyML and edge AI.
• Deploy lightweight AI models on microcontrollers.
• Optimize AI inference for low-power consumption.
• Integrate TinyML with real-world IoT applications.

This instructor-led, live training in Tashkent (online or onsite) is tailored for intermediate to advanced robotics engineers, AI developers, and automation specialists looking to implement Edge AI in robotics applications.

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

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

Edge & Lightweight Agents is a practical course focused on deploying agentic AI workloads on resource-constrained devices. Participants learn to build, optimize, and manage lightweight agents capable of local reasoning and inference, improving speed, privacy, and reliability in distributed environments. The course emphasises performance tuning, low-latency design, and hardware–software integration.

This instructor-led, live training (online or on-site) is aimed at intermediate-level professionals who wish to implement and optimise on-device agentic systems using Python and edge AI frameworks.

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

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

Format of the Course

• Interactive lecture and practical demonstrations.
• Hands-on development in local or emulated environments.
• Project-based learning and guided implementation exercises.

Course Customisation Options

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

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

Upon completing this training, participants will be able to:

• Grasp the challenges associated with running AI on energy-efficient devices.
• Optimize neural networks for low-power inference tasks.
• Apply quantization, pruning, and model compression methods.
• Deploy AI models on edge hardware with minimal power draw.

This instructor-led, live training in Tashkent (online or onsite) is aimed at intermediate-level AI developers, embedded engineers, and robotics engineers who wish to optimise 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.
• Optimise 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 Tashkent (online or onsite) is aimed at intermediate-level AI developers, machine learning engineers, and system architects who wish to optimize AI models for edge deployment.

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

• Understand the challenges and requirements of deploying AI models on edge devices.
• Apply model compression techniques to reduce the size and complexity of AI models.
• Utilize quantization methods to enhance model efficiency on edge hardware.
• Implement pruning and other optimization techniques to improve model performance.
• Deploy optimized AI models on various edge devices.

This instructor-led, live training in Tashkent (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 Tashkent (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 Tashkent (online or onsite) is aimed at 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 refers to the deployment of artificial intelligence models directly on devices and machines located at the network edge, enabling real-time decision-making with minimal latency.

This instructor-led, live training (available online or on-site) is designed for advanced-level embedded systems and IoT professionals who aim to deploy AI-powered logic and control systems in manufacturing environments where speed, reliability, and offline operation are critical.

Upon completing this training, participants will be able to:

• Understand the architecture and key benefits of edge AI systems.
• Build and optimize AI models for deployment on embedded devices.
• Leverage tools such as TensorFlow Lite and OpenVINO to achieve low-latency inference.
• Integrate edge intelligence with sensors, actuators, and industrial communication protocols.

Course Format

• Interactive lectures and group discussions.
• Extensive hands-on exercises and practical practice.
• Real-world implementation in a live lab environment.

Customisation Options

• To request a tailored version of this course, please contact us to arrange a customised training session.

This instructor-led, live training in Tashkent (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 Tashkent (online or onsite) is aimed at intermediate-level urban planners, civil engineers, and smart city project managers who wish to leverage Edge AI for smart city 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 Tashkent (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 Tashkent (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 Tashkent (available online or on-site) is designed for intermediate-level healthcare professionals, biomedical engineers, and AI developers who wish to leverage Edge AI to create 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, minimizing latency and power usage while enhancing autonomy and privacy in robotic systems.

This instructor-led, live training (online or onsite) is designed for intermediate-level 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.

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

• Grasp the fundamentals of TinyML and edge AI for 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.
• Assess performance and accuracy in real-world scenarios.

Format of the Course

• Interactive lecture and discussion.
• Hands-on practice using TinyML and edge AI toolchains.
• Practical exercises on embedded and robotic hardware platforms.

Course Customization Options

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

6G and the Intelligent Edge is a forward-looking course that explores the integration of 6G wireless technologies with edge computing, IoT ecosystems, and AI-driven data processing to support intelligent, low-latency, and adaptive infrastructures.

This instructor-led, live training (online or onsite) is aimed at intermediate-level IT architects who wish to understand and design next-generation distributed architectures leveraging the synergy of 6G connectivity and intelligent edge systems.

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

• Understand 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 for intelligent decision-making.
• Plan scalable, secure, and resilient 6G-ready edge infrastructures.
• Evaluate business and operational models enabled by 6G-edge convergence.

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 Tashkent (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 enables powerful AI inference on edge devices such as the Ascend 310. CANN provides essential tools for compiling, optimizing, and deploying models in environments with limited compute and memory resources.

This instructor-led, live training (delivered online or on-site) is designed for intermediate-level AI developers and integrators who aim to deploy and optimise 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.
• Optimise model performance for constrained compute and memory environments.
• Deploy and monitor AI applications in real-world edge scenarios.

Course Format

• Interactive lectures and live demonstrations.
• Hands-on lab sessions using edge-specific models and practical scenarios.
• Live deployment examples on virtual or physical edge hardware.

Course Customisation Options

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

This instructor-led, live training in Tashkent (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 Tashkent (online or onsite) is designed for intermediate-level IoT developers, embedded engineers, and AI practitioners who want to apply TinyML for predictive maintenance, anomaly detection, and smart sensor solutions.

Upon completing this training, participants will be able to:

• Grasp the core concepts of TinyML and its role in IoT ecosystems.
• Configure a TinyML development environment tailored for IoT projects.
• Create and deploy ML models on low-power microcontrollers.
• Apply TinyML techniques for predictive maintenance and anomaly detection.
• Refine TinyML models to achieve optimal power efficiency and memory utilization.

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

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

• Understand the core concepts of Edge AI.
• Set up and configure Edge AI environments.
• Develop, train, and optimise 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 session in Tashkent (online or onsite) is designed for intermediate-level embedded systems engineers and AI developers who aim to deploy machine learning models on microcontrollers utilizing TensorFlow Lite and Edge Impulse.

Upon completing this training, participants will be capable of:

• Grasping the core principles of TinyML and its advantages for edge AI solutions.
• Configuring a development environment suitable for TinyML initiatives.
• Training, optimizing, and deploying AI models on power-efficient microcontrollers.
• Leveraging TensorFlow Lite and Edge Impulse to create practical TinyML applications.
• Enhancing AI models for improved power efficiency and adherence to memory limitations.

Cambricon MLUs (Machine Learning Units) are specialized AI chips designed to accelerate inference and training processes in both edge computing and datacenter environments.

This instructor-led training session, available online or on-site, is designed for intermediate-level developers interested in creating and deploying AI models using the BANGPy framework and Neuware SDK on Cambricon MLU hardware.

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

• Configure and establish the development environments for BANGPy and Neuware.
• Create and optimize Python and C++ models tailored for Cambricon MLUs.
• Deploy models onto edge and data center devices operating on the Neuware runtime.
• Enhance ML workflows by integrating MLU-specific acceleration capabilities.

Course Format

• Engaging lectures coupled with interactive discussions.
• Practical, hands-on experience using BANGPy and Neuware for development and deployment.
• Guided exercises targeting optimization, integration, and testing techniques.

Customization Options

• If you require a tailored training session aligned with your specific Cambricon device model or use case, please reach out to us to arrange details.

This instructor-led, live training in Tashkent (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.

In this comprehensive course, combine the transformative potential of AI with the agility of edge computing. 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 needed to optimise AI performance for real-time processing and decision-making at the edge.