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Course Outline
Introduction to Huawei CloudMatrix
- Overview of the CloudMatrix ecosystem and deployment workflow.
- Supported models, data formats, and deployment modes.
- Typical use cases and compatible chipsets.
Preparing Models for Deployment
- Exporting models from training tools such as MindSpore, TensorFlow, and PyTorch.
- Employing ATC (Ascend Tensor Compiler) for format conversion.
- Distinguishing between static and dynamic shape models.
Deploying to CloudMatrix
- Creating services and registering models.
- Deploying inference services via the user interface or command line interface (CLI).
- Managing routing, authentication, and access control.
Serving Inference Requests
- Differentiating between batch and real-time inference flows.
- Implementing data preprocessing and postprocessing pipelines.
- Integrating CloudMatrix services into external applications.
Monitoring and Performance Tuning
- Tracking deployment logs and requests.
- Managing resource scaling and load balancing.
- Optimising latency and throughput.
Integration with Enterprise Tools
- Connecting CloudMatrix with OBS and ModelArts.
- Utilising workflows and model versioning.
- Implementing CI/CD for model deployment and rollback procedures.
End-to-End Inference Pipeline
- Deploying a complete image classification pipeline.
- Benchmarking and validating accuracy.
- Simulating failover scenarios and system alerts.
Summary and Next Steps
Requirements
- A foundational understanding of AI model training workflows.
- Practical experience with Python-based machine learning frameworks.
- Basic familiarity with cloud deployment concepts.
Target Audience
- AI operations teams.
- Machine learning engineers.
- Cloud deployment specialists working with Huawei infrastructure.
21 Hours
Testimonials (2)
The extensive selection of tools presented
Miruna Buzduga - Aeronamic Eastern Europe
Course - AI Enablement Training for Engineers
Step by step training with a lot of exercises. It was like a workshop and I am very glad about that.
