CANN for Edge AI Deployment培訓

這種以講師為主導的 香港（在線或現場）現場培訓面向希望掌握邊緣 AI 最新進展、優化其 AI 模型以進行邊緣部署並探索跨各個行業的專業應用的高級 AI 從業者、研究人員和開發人員。

在培訓結束時，參與者將能夠：

• 探索邊緣 AI 模型開發和優化中的高級技術。
• 實施在邊緣設備上部署 AI 模型的尖端策略。
• 將專用工具和框架用於高級邊緣 AI 應用程式。
• 優化邊緣 AI 解決方案的性能和效率。
• 探索邊緣 AI 的創新用例和新興趨勢。
• 解決邊緣 AI 部署中的高級道德和安全注意事項。

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 optimize 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.
• Optimize 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 Customization Options

• To request a customized 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.

這種以講師為主導的 香港（在線或現場）現場培訓面向希望獲得在邊緣設備上為各種應用程式部署 AI 模型的實用技能的中級開發人員、數據科學家和技術愛好者。

在培訓結束時，參與者將能夠：

• 了解邊緣 AI 的原理及其優勢。
• 設置和配置邊緣計算環境。
• 開發、訓練和優化用於邊緣部署的 AI 模型。
• 在邊緣設備上實施實用的 AI 解決方案。
• 評估和改進邊緣部署模型的性能。
• 解決邊緣 AI 應用程式中的道德和安全注意事項。

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

This instructor-led, live training (online or onsite) is aimed at beginner-level AI developers who wish to understand how CANN fits into the model lifecycle from training to deployment, and how it works with frameworks like 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 — 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.

CANN SDK (Compute Architecture for Neural Networks) is Huawei’s AI compute foundation that allows developers to fine-tune and optimize the performance of deployed neural networks on Ascend AI processors.

This instructor-led, live training (online or onsite) is aimed at advanced-level AI developers and system engineers who wish to optimize inference performance using CANN’s advanced toolset, including the Graph Engine, TIK, and custom operator development.

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

• Understand CANN's runtime architecture and performance lifecycle.
• Use profiling tools and Graph Engine for performance analysis and optimization.
• Create and optimize custom operators using TIK and TVM.
• Resolve memory bottlenecks and improve model throughput.

Format of the Course

• Interactive lecture and discussion.
• Hands-on labs with real-time profiling and operator tuning.
• Optimization exercises using edge-case deployment examples.

Course Customization Options

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

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

This instructor-led, live training (online or onsite) is aimed at intermediate-level AI practitioners who wish to build, deploy, and optimize vision and language models using the CANN SDK for production use cases.

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

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

Format of the Course

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

Course Customization Options

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

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

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

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

• Write and test custom AI operators using the TIK DSL for Ascend processors.
• Integrate custom ops into the CANN runtime and execution graph.
• Use TVM for operator scheduling, auto-tuning, and benchmarking.
• Debug and optimize instruction-level performance for custom computation patterns.

Format of the Course

• Interactive lecture and demonstration.
• Hands-on coding of operators using TIK and TVM pipelines.
• Testing and tuning on Ascend hardware or simulators.

Course Customization Options

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

這種由講師指導的現場香港（在線或現場）培訓面向希望利用邊緣人工智慧提供創新自主系統解決方案的中級機器人工程師、自動駕駛汽車開發人員和人工智慧研究人員。

在培訓結束時，參與者將能夠：

• 瞭解邊緣 AI 在自主系統中的作用和優勢。
• 開發和部署 AI 模型，以便在邊緣設備上進行即時處理。
• 在自動駕駛汽車、無人機和機器人技術中實施邊緣 AI 解決方案。
• 使用 Edge AI 設計和優化控制系統。
• 解決自主 AI 應用中的道德和監管考慮。

這種由講師指導的 香港（在線或現場）實時培訓面向希望全面了解邊緣 AI 從概念到實際實施（包括設置和部署）的中級開發人員和 IT 專業人員。

在培訓結束時，參與者將能夠：

• 了解邊緣 AI 的基本概念。
• 設置和配置邊緣 AI 環境。
• 開發、訓練和優化邊緣 AI 模型。
• 部署和管理邊緣 AI 應用程式。
• 將邊緣 AI 與現有系統和工作流整合。
• 解決邊緣 AI 實施中的道德考慮和最佳實踐。

這種以講師為主導的香港（在線或現場）現場培訓面向希望利用邊緣 AI 提供創新醫療保健解決方案的中級醫療保健專業人員、生物醫學工程師和 AI 開發人員。

在培訓結束時，參與者將能夠：

• 了解邊緣 AI 在醫療保健中的作用和優勢。
• 在醫療保健應用的邊緣設備上開發和部署 AI 模型。
• 在可穿戴設備和診斷工具中實施邊緣 AI 解決方案。
• 使用邊緣 AI 設計和部署患者監護系統。
• 解決醫療保健 AI 應用程式中的道德和監管考慮因素。

這種以講師為主導的 香港（在線或現場）現場培訓面向希望利用邊緣 AI 通過智慧數據處理和分析功能增強物聯網應用程式的中級開發人員、系統架構師和行業專業人士。

在培訓結束時，參與者將能夠：

• 瞭解邊緣 AI 基礎知識及其在物聯網中的應用。
• 為IoT設備設置和配置邊緣 AI 環境。
• 在邊緣設備上為IoT應用程式開發和部署 AI 模型。
• 在物聯網系統中實現即時數據處理和決策。
• 將邊緣 AI 與各種物聯網協定和平臺整合。
• 解決面向物聯網的邊緣 AI 中的道德考量和最佳實踐。

這種以講師為主導的 香港（在線或現場）現場培訓面向希望了解邊緣 AI 及其入門應用程式基礎知識的初級開發人員和 IT 專業人員。

在培訓結束時，參與者將能夠：

• 瞭解邊緣 AI 的基本概念和架構。
• 設置和配置邊緣 AI 環境。
• 開發和部署簡單的邊緣 AI 應用程式。
• 識別並了解邊緣 AI 的用例和優勢。

這種以講師為主導的 香港（在線或現場）現場培訓面向希望保護和合乎道德地部署邊緣 AI 解決方案的中級網路安全專業人員、系統管理員和 AI 倫理研究人員。

在培訓結束時，參與者將能夠：

• 瞭解邊緣 AI 中的安全和隱私挑戰。
• 實施保護邊緣設備和數據的最佳實踐。
• 制定策略以降低邊緣 AI 部署中的安全風險。
• 解決道德考慮並確保遵守法規。
• 對邊緣 AI 應用程式進行安全評估和審計。