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Course Outline

Overview of Huawei CloudMatrix

  • The CloudMatrix ecosystem and its deployment workflow
  • Compatible models, formats, and deployment configurations
  • Common applications and supported chipset types

Model Preparation for Deployment

  • Exporting models from training environments (MindSpore, TensorFlow, PyTorch)
  • Applying ATC (Ascend Tensor Compiler) for format transformation
  • Distinctions between static and dynamic shape models

Deployment to CloudMatrix

  • Creating services and registering models
  • Launching inference services via user interface or command line
  • Managing routing, authentication, and access permissions

Handling Inference Requests

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

Monitoring and Performance Optimization

  • Reviewing deployment logs and tracking request activity
  • Scaling resources and balancing load
  • Adjusting latency and enhancing throughput

Enterprise Tool Integration

  • Linking CloudMatrix with OBS and ModelArts
  • Leveraging workflows and managing model versions
  • Implementing CI/CD for model deployment and rollback strategies

Complete Inference Pipeline

  • Establishing a full image classification pipeline
  • Benchmarking performance and verifying accuracy
  • Testing failover mechanisms and system alerts

Recap and Future Directions

Requirements

  • A solid grasp of 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

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