CANN for Edge AI Deployment Training Course

This instructor-led, live training in Taiwan (online or onsite) targets intermediate-level telecom professionals, AI engineers, and IoT specialists interested in exploring how 5G networks accelerate Edge AI applications.

Upon completing this training, participants will be able to:

• Grasp the fundamentals of 5G technology and its influence on Edge AI.
• Deploy AI models optimized for low-latency applications within 5G environments.
• Implement real-time decision-making systems leveraging 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 Taiwan (online or onsite) targets 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.

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

• Investigate advanced techniques in Edge AI model development and optimization.
• Execute state-of-the-art 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.
• Address advanced ethical and security considerations in Edge AI deployments.

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

This instructor-led live training, available both online and onsite, targets intermediate-level AI engineers and data scientists eager to develop and optimize neural network models utilizing Huawei's Ascend platform alongside the CANN toolkit.

Upon completing this training, participants will be equipped to:

• Establish and configure the CANN development environment.
• Construct AI applications using MindSpore and CloudMatrix workflows.
• Enhance performance on Ascend NPUs through the use of custom operators and tiling techniques.
• Deploy models into either edge or cloud environments.

Course Format

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

Customization Options

• For customized training tailored to your specific infrastructure or datasets, please reach out to us to make arrangements.

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, available online or onsite, targets intermediate-level AI developers and engineers looking to efficiently deploy trained AI models onto Huawei Ascend hardware. The course utilizes the CANN toolkit along with tools such as MindSpore, TensorFlow, or PyTorch.

Upon completing this training, participants will be able to:

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

Course Format

• Interactive lectures combined with live demonstrations.
• Hands-on lab exercises using CANN tools and Ascend simulators or physical devices.
• Practical deployment scenarios based on real-world AI models.

Customization Options

• For requests regarding customized training for this course, please reach out to us to make arrangements.

This instructor-led, live training in Taiwan (available online or onsite) is tailored for intermediate-level developers, data scientists, and tech enthusiasts seeking 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 Taiwan (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.

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 optimized for inference and training in edge and datacenter scenarios.

This instructor-led, live training (online or onsite) is aimed at intermediate-level developers who wish to build and deploy AI models using the BANGPy framework and Neuware SDK on Cambricon MLU hardware.

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

• Set up and configure 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 running Neuware runtime.
• Integrate ML workflows with MLU-specific acceleration features.

Format of the Course

• Interactive lecture and discussion.
• Hands-on use of BANGPy and Neuware for development and deployment.
• Guided exercises focused on optimization, integration, and testing.

Course Customization Options

• To request a customized training for this course based on your Cambricon device model or use case, please contact us to arrange.

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 live training, available online or onsite, targets beginner-level AI developers who want to understand how CANN fits into the model lifecycle—from training to deployment—and how it integrates with frameworks such as MindSpore, TensorFlow, and PyTorch.

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

• Understand the purpose and architecture of the CANN toolkit.
• Set up a development environment with CANN and MindSpore.
• Convert and deploy a simple AI model to Ascend hardware.
• Gain foundational knowledge for future CANN optimization or integration projects.

Format of the Course

• Interactive lecture and discussion.
• Hands-on labs with simple model deployment.
• Step-by-step walkthrough of the CANN toolchain and integration points.

Course Customization Options

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

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

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

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

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

Course Format

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

Customization Options

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

CANN SDK (Compute Architecture for Neural Networks) serves as Huawei’s foundational AI computing platform, empowering developers to refine and maximize the performance of neural networks deployed on Ascend AI processors.

This instructor-led, live training session (available online or in-person) is designed for advanced AI developers and system engineers seeking to boost inference performance through CANN’s advanced toolkit, which includes the Graph Engine, TIK, and custom operator development capabilities.

Upon completing this training, participants will be able to:

• Grasp CANN’s runtime architecture and performance lifecycle.
• Utilize profiling tools and the Graph Engine to analyze and optimize performance.
• Develop and optimize custom operators using TIK and TVM.
• Address memory bottlenecks and enhance model throughput.

Course Format

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

Customization Options

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

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

This instructor-led, live training (available online or onsite) is designed for intermediate-level AI practitioners looking to build, deploy, and optimize vision and language models using the CANN SDK for production environments.

Upon completing this training, participants will be able to:

• Deploy and optimize CV and NLP models using CANN and AscendCL.
• Utilize CANN tools to convert models and integrate them into live pipelines.
• Optimize inference performance for tasks such as detection, classification, and sentiment analysis.
• Construct real-time CV/NLP pipelines suitable for edge or cloud-based deployment scenarios.

Format of the Course

• Interactive lecture and demonstration.
• Hands-on lab involving model deployment and performance profiling.
• Live pipeline design utilizing real-world CV and NLP use cases.

Course Customization Options

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

Leveraging CANN TIK (Tensor Instruction Kernel) alongside Apache TVM allows for sophisticated optimization and customization of AI model operators tailored to Huawei Ascend hardware.

This instructor-led live training, available online or onsite, is designed for advanced system developers seeking to construct, deploy, and fine-tune custom operators for AI models utilizing CANN’s TIK programming model and TVM compiler integration.

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

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

Course Format

• Interactive lectures and live demonstrations.
• Hands-on coding exercises involving TIK and TVM pipelines.
• Testing and tuning on Ascend hardware or simulation environments.

Customization Options

• For inquiries regarding customized training for this course, please contact us to make arrangements.

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