Course Code: aicloudmatrix
Duration: 21 hours
Prerequisites:
  • An understanding of AI model training workflows
  • Experience with Python-based ML frameworks
  • Basic familiarity with cloud deployment concepts

Audience

  • AI ops teams
  • Machine learning engineers
  • Cloud deployment specialists working with Huawei infrastructure
Overview:

CloudMatrix is Huawei’s unified AI development and deployment platform designed to support scalable, production-grade inference pipelines.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level AI professionals who wish to deploy and monitor AI models using the CloudMatrix platform with CANN and MindSpore integration.

By the end of this training, participants will be able to:

  • Use CloudMatrix for model packaging, deployment, and serving.
  • Convert and optimize models for Ascend chipsets.
  • Set up pipelines for real-time and batch inference tasks.
  • Monitor deployments and tune performance in production settings.

Format of the Course

  • Interactive lecture and discussion.
  • Hands-on use of CloudMatrix with real deployment scenarios.
  • Guided exercises focused on conversion, optimization, and scaling.

Course Customization Options

  • To request a customized training for this course based on your AI infrastructure or cloud environment, please contact us to arrange.
Course Outline:

Introduction to Huawei CloudMatrix

  • CloudMatrix ecosystem and deployment flow
  • Supported models, formats, and deployment modes
  • Typical use cases and supported chipsets

Preparing Models for Deployment

  • Model export from training tools (MindSpore, TensorFlow, PyTorch)
  • Using ATC (Ascend Tensor Compiler) for format conversion
  • Static vs dynamic shape models

Deploying to CloudMatrix

  • Service creation and model registration
  • Deploying inference services via UI or CLI
  • Routing, authentication, and access control

Serving Inference Requests

  • Batch vs real-time inference flows
  • Data preprocessing and postprocessing pipelines
  • Calling CloudMatrix services from external apps

Monitoring and Performance Tuning

  • Deployment logs and request tracking
  • Resource scaling and load balancing
  • Latency tuning and throughput optimization

Integration with Enterprise Tools

  • Connecting CloudMatrix with OBS and ModelArts
  • Using workflows and model versioning
  • CI/CD for model deployment and rollback

End-to-End Inference Pipeline

  • Deploying a complete image classification pipeline
  • Benchmarking and validating accuracy
  • Simulating failover and system alerts

Summary and Next Steps

Sites Published:

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