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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
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.