Get in Touch

Course Outline

Overview of Huawei CloudMatrix

  • CloudMatrix ecosystem and deployment lifecycle
  • Compatible models, formats, and deployment modes
  • Common use cases and compatible chipset types

Model Preparation for Deployment

  • Exporting models from training environments (MindSpore, TensorFlow, PyTorch)
  • Applying ATC (Ascend Tensor Compiler) for format transformation
  • Distinction between static and dynamic shape models

Implementing on CloudMatrix

  • Service instantiation and model registration
  • Deploying inference services via interface or command line
  • Managing routing, authentication, and access permissions

Processing Inference Requests

  • Workflows for batch versus real-time inference
  • Data preprocessing and postprocessing stages
  • Invoking CloudMatrix services from external applications

Surveillance and Performance Refinement

  • Reviewing deployment logs and tracking requests
  • Resource scaling strategies and load distribution
  • Optimizing latency and maximizing throughput

Enterprise Tool Integration

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

Complete Inference Pipeline

  • Deploying a full image classification workflow
  • Conducting benchmarks and accuracy validation
  • Simulating failover mechanisms and system alerts

Recap and Future Directions

Requirements

  • Foundational knowledge of AI model training procedures
  • Proficiency with Python-based machine learning frameworks
  • Introductory awareness of cloud deployment principles

Target Audience

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

Number of participants


Price per participant

Testimonials (2)

Upcoming Courses

Related Categories