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Course Outline
Introduction to Huawei CloudMatrix
- Overview of the CloudMatrix ecosystem and deployment workflow
- Supported models, formats, and various deployment modes
- Common use cases and compatible chipsets
Preparing Models for Deployment
- Exporting models from training tools such as MindSpore, TensorFlow, and PyTorch
- Utilizing ATC (Ascend Tensor Compiler) for format conversion
- Distinguishing between static and dynamic shape models
Deploying to CloudMatrix
- Creating services and registering models
- Deploying inference services through the UI or CLI
- Managing routing, authentication, and access controls
Serving Inference Requests
- Comparing batch and real-time inference flows
- Implementing data preprocessing and postprocessing pipelines
- Integrating CloudMatrix services into external applications
Monitoring and Performance Tuning
- Reviewing deployment logs and tracking requests
- Managing resource scaling and load balancing
- Optimizing latency and throughput performance
Integration with Enterprise Tools
- Connecting CloudMatrix with OBS and ModelArts
- Leveraging workflows and model versioning
- Implementing CI/CD for model deployment and rollback strategies
End-to-End Inference Pipeline
- Deploying a complete image classification pipeline
- Benchmarking performance and validating accuracy
- Simulating failover scenarios and system alerts
Summary and Recommended Next Steps
Requirements
- A solid grasp of AI model training workflows
- Familiarity with Python-based ML frameworks
- Fundamental knowledge of cloud deployment concepts
Target Audience
- AI operations teams
- Machine learning engineers
- Cloud deployment specialists utilizing 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.
