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Course Outline
Overview of Huawei CloudMatrix
- The CloudMatrix ecosystem and its deployment architecture
- Compatible models, file formats, and deployment strategies
- Common application scenarios and supported chipset types
Model Preparation for Deployment
- Exporting models from training frameworks (MindSpore, TensorFlow, PyTorch)
- Utilizing ATC (Ascend Tensor Compiler) for format conversion
- Distinction between static and dynamic shape models
Deployment on CloudMatrix
- Creating services and registering models
- Implementing inference services through the UI or CLI
- Managing routing, authentication, and access permissions
Handling Inference Requests
- Comparing batch and real-time inference workflows
- Designing data preprocessing and postprocessing pipelines
- Integrating CloudMatrix services with external applications
Monitoring and Performance Optimization
- Reviewing deployment logs and tracking requests
- Implementing resource scaling and load balancing strategies
- Optimizing latency and maximizing throughput
Enterprise Tool Integration
- Linking CloudMatrix with OBS and ModelArts
- Utilizing workflows and managing model versions
- Establishing CI/CD pipelines for deployment and rollback
Complete Inference Pipeline
- Deploying a full image classification workflow
- Conducting benchmarks and verifying accuracy
- Testing failover mechanisms and system alerts
Recap and Future Directions
Requirements
- Fundamental comprehension of AI model training processes
- Practical experience with Python-based machine learning frameworks
- General knowledge of cloud deployment principles
Intended Audience
- AI Operations teams
- Machine Learning Engineers
- Cloud deployment experts operating within the Huawei ecosystem
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.