Course Outline

Introduction to Huawei’s AI Ecosystem for Government

  • Overview of Ascend AI hardware: 310, 910, and 910B
  • High-level components: MindSpore, CANN, AscendCL
  • Industry positioning and architecture principles

The Role of CANN in Huawei’s AI Stack for Government

  • Definition of CANN: SDK purpose and internal layers
  • ATC, TBE, and AscendCL: compiling and executing models
  • How CANN supports inference optimization and deployment for government applications

MindSpore Overview and Architecture for Government

  • Training and inference workflows in MindSpore
  • Graph mode, PyNative, and hardware abstraction
  • Integration with Ascend NPU via CANN backend for government use

AI Lifecycle on Ascend: Training to Deployment for Government

  • Model creation in MindSpore or conversion from other frameworks
  • Exporting and compiling models using ATC for government systems
  • Deployment on Ascend hardware using OM models and AscendCL for government operations

Comparison with Other AI Stacks for Government

  • MindSpore vs. PyTorch, TensorFlow: focus and positioning for government use
  • Deployment workflows on Ascend vs. GPU-based stacks for government environments
  • Opportunities and limitations for enterprise use in government settings

Enterprise Integration Scenarios for Government

  • Use cases in smart manufacturing, government AI, and telecom sectors
  • Scalability, compliance, and ecosystem considerations for government agencies
  • Cloud/on-prem hybrid deployment using Huawei stack for government operations

Summary and Next Steps for Government

Requirements

  • Familiarity with artificial intelligence (AI) workflows or platform architecture for government use
  • Basic understanding of model training and deployment processes
  • No prior hands-on experience with CANN or MindSpore is required

Audience

  • AI platform evaluators and infrastructure architects for government agencies
  • AI/ML DevOps and pipeline integrators within the public sector
  • Technology managers and decision-makers in government organizations
 14 Hours

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