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Course Outline

Overview of Huawei CloudMatrix

  • The CloudMatrix ecosystem and deployment workflow
  • Compatible models, formats, and deployment types
  • 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 adaptation
  • Static versus dynamic shape models

Deploying on CloudMatrix

  • Creating services and registering models
  • Launching inference services through the UI or CLI
  • Managing routing, authentication, and access controls

Handling Inference Requests

  • Batch versus real-time inference workflows
  • Preprocessing and postprocessing data pipelines
  • Interfacing with CloudMatrix services from external applications

Monitoring and Performance Optimization

  • Tracking deployment logs and requests
  • Managing resource scaling and load balancing
  • Adjusting latency and optimizing throughput

Enterprise Tool Integration

  • Connecting CloudMatrix with OBS and ModelArts
  • Implementing workflows and model version control
  • CI/CD practices for model deployment and rollback

End-to-End Inference Pipeline

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

Wrap-up and Future Steps

Requirements

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

Target Audience

  • AI operations (MLOps) teams
  • Machine learning engineers
  • Cloud deployment specialists utilizing Huawei infrastructure
 21 Hours

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