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Course Outline
Introduction to Huawei CloudMatrix
- Overview of the CloudMatrix ecosystem and its deployment flow.
- 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.
- Utilizing ATC (Ascend Tensor Compiler) for format conversion.
- Understanding static versus dynamic shape models.
Deploying to CloudMatrix
- Service creation and model registration processes.
- Deploying inference services via the User Interface (UI) or Command Line Interface (CLI).
- Managing routing, authentication, and access control.
Serving Inference Requests
- Distinguishing between batch and real-time inference flows.
- Constructing data preprocessing and postprocessing pipelines.
- Invoking CloudMatrix services from external applications.
Monitoring and Performance Tuning
- Tracking request logs and deployment records.
- Implementing resource scaling and load balancing strategies.
- Optimizing throughput and tuning latency.
Integration with Enterprise Tools
- Connecting CloudMatrix with OBS and ModelArts.
- Leveraging workflows and model versioning capabilities.
- Implementing CI/CD pipelines for model deployment and rollback mechanisms.
End-to-End Inference Pipeline
- Deploying a comprehensive image classification pipeline.
- Benchmarking results and validating accuracy.
- Simulating system alerts and failover procedures.
Summary and Next Steps
Requirements
- A foundational understanding of AI model training workflows.
- Prior experience working with Python-based ML frameworks.
- Basic familiarity with cloud deployment concepts.
Target Audience
- AI operations teams.
- Machine learning engineers.
- Cloud deployment specialists leveraging 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.
