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Duration 21 hours
Course Outline
Introduction to Huawei CloudMatrix
- The CloudMatrix ecosystem and deployment workflow
- Compatible models, formats, and deployment methodologies
- Common use cases and supported chipsets
Preparing Models for Deployment
- Exporting models from training tools (MindSpore, TensorFlow, PyTorch)
- Utilising ATC (Ascend Tensor Compiler) for format conversion
- Models with static versus dynamic shapes
Deploying to CloudMatrix
- Creating services and registering models
- Deploying inference services via UI or CLI
- Managing 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 Tuning
- Reviewing deployment logs and request tracking
- Resource scaling and load balancing strategies
- Latency adjustment and throughput optimisation
Integration with Enterprise Tools
- Linking CloudMatrix with OBS and ModelArts
- Utilising workflows and model versioning
- CI/CD for model deployment and rollback
End-to-End Inference Pipeline
- Implementing a complete image classification pipeline
- Benchmarking and validating accuracy
- Simulating failover and system alerts
Summary and Next Steps
Requirements
- Familiarity with AI model training processes
- Proficiency with Python-based ML frameworks
- Fundamental understanding of cloud deployment principles
Target Audience
- AI operations teams
- Machine learning engineers
- Cloud deployment specialists engaged with Huawei infrastructure
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.
