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Course Outline

Introduction to Huawei CloudMatrix

  • The CloudMatrix ecosystem and its deployment workflow
  • Compatible models, formats, and deployment modes
  • Common 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
  • Distinctions between static and 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

  • Comparing batch and real-time inference flows
  • Implementing data preprocessing and postprocessing pipelines
  • Integrating CloudMatrix services into external applications

Monitoring and Performance Tuning

  • Analyzing deployment logs and tracking requests
  • Managing resource scaling and load balancing
  • Optimizing latency and throughput

Integration with Enterprise Tools

  • Linking CloudMatrix with OBS and ModelArts
  • Implementing workflows and model versioning
  • Establishing CI/CD for model deployment and rollback

End-to-End Inference Pipeline

  • Deploying a complete image classification pipeline
  • Benchmarking and validating accuracy
  • Simulating failover scenarios and system alerts

Summary and Next Steps

Requirements

  • A solid grasp of AI model training workflows
  • Experience working with Python-based machine learning frameworks
  • Fundamental knowledge of cloud deployment concepts

Target Audience

  • AI operations teams
  • Machine learning engineers
  • Cloud deployment specialists collaborating with Huawei infrastructure
 21 Hours

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