Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Introduction to Huawei’s AI Ecosystem
- Overview of Ascend AI hardware: models 310, 910, and 910B.
- High-level components: MindSpore, CANN, and AscendCL.
- Industry positioning and architectural principles.
The Role of CANN in Huawei’s AI Stack
- What is CANN? SDK purpose and internal layers.
- ATC, TBE, and AscendCL: compiling and executing models.
- How CANN supports inference optimization and deployment.
MindSpore Overview and Architecture
- Training and inference workflows in MindSpore.
- Graph mode, PyNative, and hardware abstraction.
- Integration with Ascend NPU via the CANN backend.
AI Lifecycle on Ascend: From Training to Deployment
- Model creation in MindSpore or conversion from other frameworks.
- Exporting and compiling models using ATC.
- Deployment on Ascend hardware using OM models and AscendCL.
Comparison with Other AI Stacks
- MindSpore vs. PyTorch, TensorFlow: focus and positioning.
- Deployment workflows on Ascend vs. GPU-based stacks.
- Opportunities and limitations for enterprise use.
Enterprise Integration Scenarios
- Use cases in smart manufacturing, government AI, and telecom.
- Scalability, compliance, and ecosystem considerations.
- Cloud/on-prem hybrid deployment using Huawei stack.
Summary and Next Steps
Requirements
- Familiarity with AI workflows or platform architecture.
- Basic understanding of model training and deployment.
- No prior hands-on experience with CANN or MindSpore is required.
Audience
- AI platform evaluators and infrastructure architects.
- AI/ML DevOps engineers and pipeline integrators.
- Technology managers and decision-makers.
14 Hours
