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Course Outline
Introduction to Huawei CloudMatrix
- Overview of the CloudMatrix ecosystem and deployment flow
- Supported models, formats, and deployment modes
- Typical use cases and supported chipsets
Preparing Models for Deployment
- Exporting models from training tools such as MindSpore, TensorFlow, and PyTorch
- Employing 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 via UI or CLI
- Configuring routing, authentication, and access control
Serving Inference Requests
- Differentiating between batch and real-time inference flows
- Constructing data preprocessing and postprocessing pipelines
- Integrating CloudMatrix services into external applications
Monitoring and Performance Tuning
- Tracking deployment logs and requests
- Managing resource scaling and load balancing
- Optimizing latency and throughput
Integration with Enterprise Tools
- Connecting CloudMatrix with OBS and ModelArts
- Utilizing workflows and model versioning
- Implementing CI/CD for model deployment and rollback
End-to-End Inference Pipeline
- Deploying a comprehensive image classification pipeline
- Benchmarking and validating accuracy
- Simulating failover scenarios and system alerts
Summary and Next Steps
Requirements
- A foundational understanding of AI model training workflows
- Experience working with Python-based machine learning frameworks
- Basic familiarity with 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.
