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