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