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Course Outline
Overview of Huawei CloudMatrix
- The CloudMatrix ecosystem and its deployment architecture.
- Compatible models, data formats, and deployment strategies.
- Common use cases and supported chipset types.
Preparing Models for Deployment
- Exporting models from training frameworks (MindSpore, TensorFlow, PyTorch).
- Utilizing ATC (Ascend Tensor Compiler) for format conversion.
- Distinguishing between static and dynamic shape models.
Deployment on CloudMatrix
- Creating services and registering models.
- Deploying inference services through UI or CLI interfaces.
- Configuring routing, authentication, and access controls.
Handling Inference Requests
- Comparing batch and real-time inference workflows.
- Building data preprocessing and postprocessing pipelines.
- Integrating CloudMatrix services into external applications.
Monitoring and Performance Optimization
- Analyzing deployment logs and tracking requests.
- Managing resource scaling and load balancing.
- Refining latency and optimizing throughput.
Enterprise Tool Integration
- Linking CloudMatrix with OBS and ModelArts.
- Implementing workflows and model version control.
- Establishing CI/CD pipelines for model deployment and rollback.
End-to-End Inference Pipeline
- Deploying a comprehensive image classification pipeline.
- Conducting benchmarks and accuracy validation.
- Simulating failover scenarios and system alerts.
Recap and Future Directions
Requirements
- A foundational grasp of AI model training processes.
- Proficiency with Python-based machine learning frameworks.
- Fundamental knowledge of cloud deployment principles.
Intended Audience
- AI operations teams.
- Machine learning engineers.
- Cloud deployment experts working within Huawei’s 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.
