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Course Outline

Overview of Huawei CloudMatrix

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

Model Preparation for Deployment

  • Exporting models from training frameworks (MindSpore, TensorFlow, PyTorch)
  • Utilizing ATC (Ascend Tensor Compiler) for format conversion
  • Distinction between static and dynamic shape models

Deployment on CloudMatrix

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

Handling Inference Requests

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

Monitoring and Performance Optimization

  • Reviewing deployment logs and tracking requests
  • Implementing resource scaling and load balancing strategies
  • Optimizing latency and maximizing throughput

Enterprise Tool Integration

  • Linking CloudMatrix with OBS and ModelArts
  • Utilizing workflows and managing model versions
  • Establishing CI/CD pipelines for deployment and rollback

Complete Inference Pipeline

  • Deploying a full image classification workflow
  • Conducting benchmarks and verifying accuracy
  • Testing failover mechanisms and system alerts

Recap and Future Directions

Requirements

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

Intended Audience

  • AI Operations teams
  • Machine Learning Engineers
  • Cloud deployment experts operating within the Huawei ecosystem
 21 Hours

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Provisional Upcoming Courses (Require 5+ participants)

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