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Course Outline
Introduction to Huawei CloudMatrix
- The CloudMatrix ecosystem and deployment workflow
- Supported models, formats, and deployment modes
- Typical use cases and supported chipsets
Preparing Models for Deployment
- Exporting models from training tools (MindSpore, TensorFlow, PyTorch)
- Using ATC (Ascend Tensor Compiler) for format conversion
- Static versus dynamic shape models
Deploying to CloudMatrix
- Service creation and model registration
- Deploying inference services via the UI or CLI
- Routing, authentication, and access control
Serving Inference Requests
- Batch versus real-time inference flows
- Data preprocessing and postprocessing pipelines
- Invoking CloudMatrix services from external applications
Monitoring and Performance Tuning
- Deployment logs and request tracking
- Resource scaling and load balancing
- Latency tuning and throughput optimisation
Integration with Enterprise Tools
- Connecting CloudMatrix with OBS and ModelArts
- Leveraging workflows and model versioning
- CI/CD for model deployment and rollback
End-to-End Inference Pipeline
- Deploying a complete image classification pipeline
- Benchmarking and validating accuracy
- Simulating failover scenarios and system alerts
Summary and Next Steps
Requirements
- A solid understanding of AI model training workflows
- Hands-on experience with Python-based machine learning frameworks
- Basic familiarity with cloud deployment concepts
Audience
- AI operations teams
- Machine learning engineers
- Cloud deployment specialists working with Huawei infrastructure
21 Hours
Testimonials (2)
The extensive selection of tools presented
Miruna Buzduga - Aeronamic Eastern Europe
Course - AI Enablement Training for Engineers
Step by step training with a lot of exercises. It was like a workshop and I am very glad about that.
