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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