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Course Outline
Overview of Huawei CloudMatrix
- The CloudMatrix ecosystem and its deployment architecture
- Compatible models, file formats, and deployment methods
- Common use cases and supported chipset types
Model Preparation for Deployment
- Exporting models from training tools such as MindSpore, TensorFlow, and PyTorch
- Utilizing ATC (Ascend Tensor Compiler) for format conversion
- Differences between static and dynamic shape models
Deployment on CloudMatrix
- Service creation and model registration processes
- Deploying inference services through the UI or CLI
- Configuring routing, authentication, and access controls
Handling Inference Requests
- Comparing batch and real-time inference workflows
- Implementing data preprocessing and postprocessing pipelines
- Integrating CloudMatrix services with external applications
Monitoring and Performance Optimization
- Analyzing deployment logs and tracking requests
- Managing resource scaling and load balancing
- Tuning for latency reduction and throughput improvement
Enterprise Tool Integration
- Linking CloudMatrix with OBS and ModelArts
- Implementing workflows and managing model versions
- Establishing CI/CD processes for model deployment and rollback
Complete Inference Pipeline
- Deploying a full image classification pipeline
- Conducting benchmarks and accuracy validation
- Simulating failover scenarios and system alerts
Recap and Future Directions
Requirements
- Foundational knowledge of AI model training workflows
- Proficiency with Python-based machine learning frameworks
- Basic 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.
