CANN for Edge AI Deployment Training Course

This instructor-led, live training in South Korea (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 with edge computing, IoT ecosystems, and AI-powered data processing to build intelligent, adaptive, and low-latency infrastructures.

Designed for intermediate-level IT architects, this instructor-led live training (available online or onsite) helps participants understand and design next-generation distributed architectures that leverage the synergy between 6G connectivity and intelligent edge systems.

Upon completion, participants will be able to:

• Grasp how 6G will reshape edge computing and IoT architectures.
• Design distributed systems optimized for 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 infrastructures ready for 6G adoption.
• Evaluate the business and operational models facilitated by the 6G-edge convergence.

Course Format

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

Customization Options

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

This instructor-led, live training in South Korea (online or onsite) is designed for 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:

• Investigate advanced techniques in the development and optimization of Edge AI models.
• Apply innovative strategies for deploying AI models on edge devices.
• Leverage specialized tools and frameworks for advanced Edge AI applications.
• Enhance the performance and efficiency of Edge AI solutions.
• Discover innovative use cases and emerging trends in Edge AI.
• Tackle advanced ethical and security challenges in Edge AI deployments.

Huawei Ascend comprises a series of AI processors engineered for high-performance model training and inference.

This instructor-led live training, available online or on-site, targets intermediate-level AI engineers and data scientists aiming to create and refine neural network models using Huawei’s Ascend platform alongside the CANN toolkit.

Upon completing this course, participants will be capable of:

• Configuring and establishing the CANN development environment.
• Creating AI applications utilizing MindSpore and CloudMatrix workflows.
• Enhancing performance on Ascend NPUs through tiling and custom operators.
• Deploying models across cloud or edge environments.

Course Format

• Interactive discussions and lectures.
• Practical application of Huawei Ascend and CANN toolkit within sample projects.
• Guided exercises centered on model building, training, and deployment.

Customization Options

• For customized training tailored to your specific infrastructure or datasets, please reach out to us to arrange a session.

CANN (Compute Architecture for Neural Networks) serves as Huawei's AI computing stack, designed for deploying and optimizing AI models on Ascend AI processors.

This instructor-led, live training session (available online or onsite) is designed for intermediate-level AI developers and engineers who want to efficiently deploy trained AI models onto Huawei Ascend hardware using the CANN toolkit. The curriculum covers integration with popular frameworks such as MindSpore, TensorFlow, and PyTorch.

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

• Comprehend the CANN architecture and its function within the AI deployment pipeline.
• Convert and adapt models from mainstream frameworks into Ascend-compatible formats.
• Utilize tools such as ATC, OM model conversion utilities, and MindSpore for both edge and cloud inference.
• Troubleshoot deployment challenges and optimize performance on Ascend hardware.

Course Format

• Interactive lectures and live demonstrations.
• Hands-on laboratory exercises utilizing CANN tools, Ascend simulators, or physical devices.
• Practical deployment scenarios based on real-world AI models.

Course Customization Options

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

This instructor-led, live training in South Korea (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 South Korea (online or onsite) is designed for advanced-level cybersecurity professionals, AI engineers, and IoT developers who wish to implement robust security measures and resilience strategies for Edge AI systems.

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

• Comprehend the security risks and vulnerabilities associated with Edge AI deployments.
• Apply encryption and authentication techniques to ensure data protection.
• Design resilient Edge AI architectures capable of withstanding cyber threats.
• Execute secure AI model deployment strategies within edge environments.

Cambricon MLUs (Machine Learning Units) are specialized AI accelerators engineered for optimized inference and training across both edge computing and datacenter environments.

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

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

• Install and configure the BANGPy and Neuware development environments.
• Develop and optimize Python- and C++-based models specifically for Cambricon MLUs.
• Deploy models to edge and datacenter devices operating on the Neuware runtime.
• Integrate machine learning workflows with MLU-specific acceleration capabilities.

Course Format

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

Customization Options

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

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

This guided, live training (available online or in-person) targets beginner AI developers looking to understand how CANN integrates into the model lifecycle, from training through deployment, and its interaction with frameworks such as MindSpore, TensorFlow, and PyTorch.

Upon completion of 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 basic AI model onto Ascend hardware.
• Acquire foundational knowledge for future CANN optimization and integration projects.

Course Format

• Interactive lectures and discussions.
• Practical labs involving simple model deployment.
• Step-by-step walkthroughs of the CANN toolchain and integration points.

Customization Options

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

Huawei’s AI stack — from the low-level CANN SDK to the high-level MindSpore framework — offers a tightly integrated AI development and deployment environment optimized for Ascend hardware.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level technical professionals who wish to understand how the CANN and MindSpore components work together to support AI lifecycle management and infrastructure decisions.

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

• Understand the layered architecture of Huawei’s AI compute stack.
• Identify how CANN supports model optimization and hardware-level deployment.
• Evaluate the MindSpore framework and toolchain in relation to industry alternatives.
• Position Huawei's AI stack within enterprise or cloud/on-prem environments.

Format of the Course

• Interactive lecture and discussion.
• Live system demos and case-based walkthroughs.
• Optional guided labs on model flow from MindSpore to CANN.

Course Customization Options

• To request a 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 framework, empowering developers to fine-tune and maximize the performance of neural networks deployed on Ascend AI processors.

This instructor-led training, available in online or onsite formats, is designed for advanced AI developers and system engineers seeking to boost inference performance through CANN's sophisticated toolset. Key components covered include the Graph Engine, TIK, and custom operator development.

Upon completing this training, participants will be equipped to:

• Grasp CANN's runtime architecture and performance lifecycle.
• Leverage profiling tools and the Graph Engine for detailed performance analysis and optimization.
• Develop and optimize custom operators using TIK and TVM.
• Address memory bottlenecks and enhance model throughput.

Training Format

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

Customization Options

• For personalized training arrangements, please contact us directly.

The CANN SDK (Compute Architecture for Neural Networks) delivers robust deployment and optimization capabilities for real-time AI solutions in computer vision and natural language processing, particularly on Huawei Ascend hardware.

This instructor-led live training, available online or onsite, targets intermediate AI professionals seeking to build, deploy, and optimize vision and language models using the CANN SDK for production environments.

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

• Deploy and optimize CV and NLP models utilizing CANN and AscendCL.
• Leverage CANN tools to convert models and integrate them into operational pipelines.
• Enhance inference performance for tasks such as detection, classification, and sentiment analysis.
• Construct real-time CV/NLP pipelines tailored for edge or cloud-based deployment scenarios.

Course Format

• Interactive lectures combined with live demonstrations.
• Hands-on labs focused on model deployment and performance profiling.
• Practical pipeline design exercises using real-world CV and NLP use cases.

Customization Options

• For customized training arrangements, please reach out to us.

By leveraging CANN TIK (Tensor Instruction Kernel) alongside Apache TVM, advanced optimization and customization of AI model operators tailored for Huawei Ascend hardware become achievable.

This instructor-led, live training session, available either online or on-site, is specifically designed for advanced-level system developers aiming to develop, deploy, and fine-tune custom operators for AI models by utilizing CANN’s TIK programming model and integrating with the TVM compiler.

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

• Write and evaluate custom AI operators using the TIK DSL for Ascend processors.
• Seamlessly integrate custom operators into the CANN runtime and execution graph.
• Utilize TVM for operator scheduling, automatic tuning, and performance benchmarking.
• Debug and optimize instruction-level performance to address specific custom computation patterns.

Course Format

• Engaging lectures combined with live demonstrations.
• Practical coding exercises focused on implementing operators via TIK and TVM pipelines.
• Testing and tuning procedures conducted on Ascend hardware or simulators.

Customization Options

• To arrange a customized version of this course, please reach out to us for further details.

This instructor-led, live training in South Korea (online or onsite) is designed for beginner to intermediate-level agritech professionals, IoT specialists, and AI engineers who aim to develop and deploy Edge AI solutions for smart farming.

Upon completing this training, participants will be able to:

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

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

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

• Grasp the role and advantages of Edge AI within autonomous systems.
• Create and deploy AI models for real-time processing on edge devices.
• Implement Edge AI solutions for autonomous vehicles, drones, and robotics.
• Design and optimize control systems utilizing Edge AI.
• Navigate ethical and regulatory considerations in autonomous AI applications.