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Course Outline

Overview of Huawei CloudMatrix

  • The CloudMatrix ecosystem and deployment architecture
  • Compatible models, file formats, and deployment strategies
  • Common applications and supported chipset types

Model Preparation for Deployment

  • Exporting models from training frameworks (MindSpore, TensorFlow, PyTorch)
  • Employing ATC (Ascend Tensor Compiler) for format transformation
  • Distinguishing between static and dynamic shape models

Deployment on CloudMatrix

  • Creating services and registering models
  • Launching inference services via the User Interface or Command Line Interface
  • Configuring routing, authentication, and access permissions

Handling Inference Requests

  • Comparing batch and real-time inference processes
  • 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 distribution
  • Adjusting latency and enhancing throughput

Integration with Enterprise Solutions

  • Linking CloudMatrix with OBS and ModelArts
  • Leveraging workflows and model version control
  • Implementing CI/CD for model deployment and rollback processes

Complete Inference Pipeline

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

Conclusion and Future Directions

Requirements

  • Familiarity with AI model training workflows
  • Proficiency with Python-based machine learning frameworks
  • Foundational knowledge of cloud deployment concepts

Target Audience

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

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