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Course Outline
Introduction to Huawei CloudMatrix
- CloudMatrix ecosystem and deployment workflow.
- Supported models, formats, and deployment modes.
- Typical use cases and supported chipsets.
Preparing Models for Deployment
- Exporting models from training tools (MindSpore, TensorFlow, PyTorch).
- Utilizing ATC (Ascend Tensor Compiler) for format conversion.
- Static versus dynamic shape models.
Deploying to CloudMatrix
- Creating services and registering models.
- Deploying inference services via UI or CLI.
- Managing routing, authentication, and access control.
Serving Inference Requests
- Batch versus real-time inference flows.
- Data preprocessing and postprocessing pipelines.
- Invoking CloudMatrix services from external applications.
Monitoring and Performance Tuning
- Tracking deployment logs and requests.
- Resource scaling and load balancing.
- Optimizing latency and throughput.
Integration with Enterprise Tools
- Connecting CloudMatrix with OBS and ModelArts.
- Managing workflows and model versioning.
- Implementing CI/CD for model deployment and rollback.
End-to-End Inference Pipeline
- Deploying a complete image classification pipeline.
- Benchmarking and validating accuracy.
- Simulating failover and system alerts.
Summary and Next Steps
Requirements
- Understanding of AI model training workflows.
- Experience with Python-based machine learning frameworks.
- Basic familiarity with cloud deployment concepts.
Audience
- AI operations teams.
- Machine learning engineers.
- Cloud deployment specialists working with 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.
