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Course Outline

Overview of Huawei CloudMatrix

  • Understanding the CloudMatrix ecosystem and deployment architecture.
  • Reviewing supported models, formats, and deployment strategies.
  • Exploring common use cases and compatible chipsets.

Model Preparation for Deployment

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

CloudMatrix Deployment Process

  • Creating services and registering models within the platform.
  • Deploying inference services using both UI and CLI interfaces.
  • Managing routing, authentication, and access control policies.

Handling Inference Requests

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

Monitoring and Performance Optimization

  • Analyzing deployment logs and tracking request patterns.
  • Managing resource scaling and load balancing strategies.
  • Refining latency and optimizing overall throughput.

Enterprise Tool Integration

  • Linking CloudMatrix with OBS and ModelArts for broader ecosystem compatibility.
  • Leveraging workflows and implementing model versioning.
  • Establishing CI/CD practices for model deployment and rollback.

Comprehensive Inference Pipeline

  • Executing the deployment of a full image classification pipeline.
  • Conducting benchmarks and validating model accuracy.
  • Simulating failover scenarios and configuring system alerts.

Recap and Future Actions

Requirements

  • A solid grasp of AI model training processes
  • Practical experience with Python-based ML frameworks
  • Foundational knowledge of cloud deployment principles

Target Audience

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

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