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Course Outline
Overview of Huawei CloudMatrix
- The CloudMatrix ecosystem and deployment workflow
- Compatible models, formats, and deployment methods
- Common use cases and compatible chipsets
Model Preparation for Deployment
- Exporting models from training tools (MindSpore, TensorFlow, PyTorch)
- Utilizing ATC (Ascend Tensor Compiler) for format conversion
- Handling static versus dynamic shape models
Deployment to CloudMatrix
- Creating services and registering models
- Launching inference services via UI or CLI
- Configuring routing, authentication, and access controls
Handling Inference Requests
- Batch versus real-time inference workflows
- Data preprocessing and postprocessing pipelines
- Invoking CloudMatrix services from external applications
Monitoring and Performance Optimization
- Reviewing deployment logs and tracking requests
- Resource scaling and load balancing strategies
- Latency adjustment and throughput enhancement
Enterprise Tool Integration
- Linking CloudMatrix with OBS and ModelArts
- Utilizing workflows and model versioning
- CI/CD practices for model deployment and rollback
End-to-End Inference Pipeline
- Implementing a complete image classification pipeline
- Benchmarking and verifying accuracy
- Simulating failover scenarios and system alerts
Conclusion and Future Steps
Requirements
- Familiarity with AI model training processes
- Proficiency with Python-based machine learning frameworks
- Foundational knowledge of cloud deployment principles
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.
