CANN for Edge AI Deployment Training Course

This instructor-led, live training in Uzbekistan (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.

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 Uzbekistan (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 Ascend is a family of AI processors designed for high-performance inference and training.

This instructor-led, live training (online or onsite) is aimed at intermediate-level AI engineers and data scientists who wish to develop and optimise neural network models using Huawei's Ascend platform and the CANN toolkit.

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

• Set up and configure the CANN development environment.
• Develop AI applications using MindSpore and CloudMatrix workflows.
• Optimise performance on Ascend NPUs using custom operators and tiling.
• Deploy models to edge or cloud environments.

Format of the Course

• Interactive lecture and discussion.
• Hands-on use of Huawei Ascend and CANN toolkit in sample applications.
• Guided exercises focused on model building, training, and deployment.

Course Customisation Options

• To request a customised training for this course based on your infrastructure or datasets, please contact us to arrange.

CANN (Compute Architecture for Neural Networks) is Huawei’s AI compute stack for deploying and optimizing AI models on Ascend AI processors.

This instructor-led, live training (online or onsite) is aimed at intermediate-level AI developers and engineers who wish to deploy trained AI models efficiently to Huawei Ascend hardware using the CANN toolkit and tools such as MindSpore, TensorFlow, or PyTorch.

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

• Understand the CANN architecture and its role in the AI deployment pipeline.
• Convert and adapt models from popular frameworks to Ascend-compatible formats.
• Use tools like ATC, OM model conversion, and MindSpore for edge and cloud inference.
• Diagnose deployment issues and optimize performance on Ascend hardware.

Format of the Course

• Interactive lecture and demonstration.
• Hands-on lab work using CANN tools and Ascend simulators or devices.
• Practical deployment scenarios based on real-world AI models.

Course Customization Options

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

This instructor-led, live training in Uzbekistan (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 Uzbekistan (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.

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.

CANN (Compute Architecture for Neural Networks) is Huawei’s AI computing toolkit designed to compile, optimize, and deploy AI models on Ascend AI processors.

This instructor-led training, available online or onsite, is tailored for beginner-level AI developers seeking to understand how CANN integrates into the model lifecycle from training to deployment, and how it works alongside frameworks such as MindSpore, TensorFlow, and PyTorch.

Upon completing this training, participants will be able to:

• Grasp the purpose and architecture of the CANN toolkit.
• Configure a development environment using CANN and MindSpore.
• Convert and deploy a simple AI model to Ascend hardware.
• Acquire foundational knowledge for future CANN optimization or integration initiatives.

Course Format

• Interactive lectures and discussions.
• Practical labs focused on simple model deployment.
• Step-by-step guidance through the CANN toolchain and integration points.

Course Customization Options

• For customized training requests, please contact us to arrange details.

Huawei’s AI stack provides a tightly integrated environment for developing and deploying AI solutions, optimized for Ascend hardware, spanning from the low-level CANN SDK to the high-level MindSpore framework.

This live, instructor-led training (available online or onsite) targets technical professionals at beginner to intermediate levels who want to understand how CANN and MindSpore collaborate to support AI lifecycle management and infrastructure decision-making.

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

• Grasp the layered architecture of Huawei’s AI compute stack.
• Recognize how CANN facilitates model optimization and hardware-level deployment.
• Assess the MindSpore framework and its toolchain in comparison with industry alternatives.
• Determine where Huawei's AI stack fits within enterprise or cloud/on-premises environments.

Course Format

• Interactive lectures and discussions.
• Live system demonstrations and case-based walkthroughs.
• Optional guided labs covering the model flow from MindSpore to CANN.

Course Customization Options

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

The CANN SDK (Compute Architecture for Neural Networks) serves as Huawei’s foundational AI computing platform, enabling developers to fine-tune and optimize the performance of neural networks deployed on Ascend AI processors.

This instructor-led live training, available both online and on-site, is designed for advanced AI developers and system engineers looking to enhance inference performance using CANN’s sophisticated tools, such as the Graph Engine, TIK, and custom operator development capabilities.

Upon completing this training, participants will be able to:

• Comprehend CANN’s runtime architecture and its performance lifecycle.
• Leverage profiling tools and the Graph Engine for performance analysis and optimization.
• Develop and optimize custom operators utilizing TIK and TVM.
• Address memory bottlenecks and boost model throughput.

Course Format

• Interactive lectures and discussions.
• Practical labs featuring real-time profiling and operator tuning.
• Optimization exercises based on edge-case deployment scenarios.

Customization Options

• To request a tailored version of this course, please contact us to arrange your specific needs.

The CANN SDK (Compute Architecture for Neural Networks) offers powerful deployment and optimisation tools for real-time AI applications in computer vision and natural language processing (NLP), particularly on Huawei Ascend hardware.

This instructor-led, live training (available online or on-site) is designed for intermediate-level AI practitioners who aim to build, deploy, and optimise vision and language models using the CANN SDK for production-grade use cases.

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

• Deploy and optimise computer vision (CV) and NLP models using CANN and AscendCL.
• Leverage CANN tools to convert models and integrate them into live pipelines.
• Optimise inference performance for tasks such as object detection, classification, and sentiment analysis.
• Construct real-time CV/NLP pipelines tailored for edge or cloud-based deployment scenarios.

Course Format

• Interactive lectures and demonstrations.
• Practical labs covering model deployment and performance profiling.
• Live pipeline design using real-world CV and NLP use cases.

Course Customisation Options

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

Leveraging CANN TIK (Tensor Instruction Kernel) and Apache TVM allows for advanced optimization and customization of AI model operators on Huawei Ascend hardware.

This instructor-led live training (available online or onsite) is designed for advanced system developers looking to build, deploy, and tune custom operators for AI models using CANN’s TIK programming model and TVM compiler integration.

Upon completing this training, participants will be capable of:

• Writing and testing custom AI operators using the TIK DSL for Ascend processors.
• Integrating custom operators into the CANN runtime and execution graph.
• Utilizing TVM for operator scheduling, auto-tuning, and benchmarking.
• Debugging and optimizing instruction-level performance for custom computational patterns.

Course Format

• Interactive lectures and demonstrations.
• Hands-on coding exercises for operators using TIK and TVM pipelines.
• Testing and tuning on Ascend hardware or simulators.

Customization Options

• To request a customized training session, please contact us to make arrangements.

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.

This instructor-led, live training in Uzbekistan (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.