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Course Outline

Introduction to Huawei CloudMatrix

  • The CloudMatrix ecosystem and deployment workflow
  • Compatible models, formats, and deployment modes
  • Common use cases and supported chipsets

Model Preparation for Deployment

  • Exporting models from training tools such as MindSpore, TensorFlow, and PyTorch
  • Employing ATC (Ascend Tensor Compiler) for format conversion
  • Handling models with static versus dynamic shapes

Deployment on CloudMatrix

  • Creating services and registering models
  • Deploying inference services using the UI or CLI
  • Managing routing, authentication, and access control

Handling Inference Requests

  • Distinguishing between batch and real-time inference flows
  • Implementing data preprocessing and postprocessing pipelines
  • Integrating CloudMatrix services with external applications

Monitoring and Performance Optimization

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

Enterprise Tool Integration

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

Complete Inference Pipeline

  • Deploying a full image classification pipeline
  • Conducting benchmarks and validating accuracy
  • Simulating failover scenarios and system alerts

Conclusion and Future Steps

Requirements

  • Familiarity with AI model training processes
  • Practical experience with Python-based machine learning frameworks
  • Fundamental knowledge of cloud deployment principles

Target Audience

  • AI operations teams
  • Machine learning engineers
  • Cloud deployment experts working with Huawei infrastructure
 21 Hours

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