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Course Outline

Overview of Huawei CloudMatrix

  • The CloudMatrix ecosystem and its deployment architecture
  • Compatible models, file formats, and deployment modes
  • Common use cases and supported chipset options

Model Preparation for Deployment

  • Exporting models from training environments (MindSpore, TensorFlow, PyTorch)
  • Utilizing ATC (Ascend Tensor Compiler) for format transformation
  • Differentiating between static and 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

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

Monitoring and Performance Optimization

  • Analyzing deployment logs and tracking request activity
  • Implementing resource scaling and load balancing strategies
  • Refining latency and enhancing throughput

Enterprise Tool Integration

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

Full-Stack Inference Pipeline

  • Building a complete image classification pipeline
  • Conducting benchmarking and accuracy validation
  • Testing failover mechanisms and system alerting

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 (AIOps) teams
  • Machine Learning Engineers
  • Cloud deployment experts utilizing Huawei infrastructure
 21 Hours

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