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Course Outline
Overview of Huawei CloudMatrix
- The CloudMatrix ecosystem and its deployment workflow
- Compatible models, formats, and deployment configurations
- Common applications and supported chipset types
Model Preparation for Deployment
- Exporting models from training environments (MindSpore, TensorFlow, PyTorch)
- Applying ATC (Ascend Tensor Compiler) for format transformation
- Distinctions between static and dynamic shape models
Deployment to CloudMatrix
- Creating services and registering models
- Launching inference services via user interface or command line
- Managing routing, authentication, and access permissions
Handling Inference Requests
- Comparing batch and real-time inference processes
- Implementing data preprocessing and postprocessing pipelines
- Integrating CloudMatrix services into external applications
Monitoring and Performance Optimization
- Reviewing deployment logs and tracking request activity
- Scaling resources and balancing load
- Adjusting latency and enhancing throughput
Enterprise Tool Integration
- Linking CloudMatrix with OBS and ModelArts
- Leveraging workflows and managing model versions
- Implementing CI/CD for model deployment and rollback strategies
Complete Inference Pipeline
- Establishing a full image classification pipeline
- Benchmarking performance and verifying accuracy
- Testing failover mechanisms and system alerts
Recap and Future Directions
Requirements
- A solid grasp of AI model training processes
- Proficiency with Python-based machine learning frameworks
- Foundational knowledge 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.