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Duration 21 hours
Course Outline
Overview of Huawei CloudMatrix
- The CloudMatrix ecosystem and its deployment workflow
- Supported model types, formats, and deployment configurations
- Common use cases and compatible chipsets
Model Preparation for Deployment
- Exporting models from training frameworks (MindSpore, TensorFlow, PyTorch)
- Utilizing ATC (Ascend Tensor Compiler) for format transformation
- Distinctions between static and dynamic shape models
Deployment to CloudMatrix
- Creating services and registering models
- Implementing inference services via UI or command-line interface
- Managing routing, authentication, and access controls
Handling Inference Requests
- Comparing batch and real-time inference workflows
- Establishing data preprocessing and postprocessing pipelines
- Integrating CloudMatrix services with external applications
Monitoring and Performance Optimization
- Reviewing deployment logs and tracking requests
- Scaling resources and implementing load balancing
- Adjusting latency and enhancing throughput
Enterprise Tool Integration
- Linking CloudMatrix with OBS and ModelArts
- Utilizing workflows and managing model versions
- Implementing CI/CD pipelines for deployment and rollback procedures
Complete Inference Pipeline
- Deploying a full image classification pipeline
- Conducting benchmarks and validating accuracy
- Simulating failover scenarios and system alerts
Recap and Future Directions
Requirements
- Foundational knowledge of AI model training workflows
- Practical experience with Python-based machine learning frameworks
- Fundamental understanding of cloud deployment principles
Target Audience
- AI operations teams
- Machine learning engineers
- Cloud deployment specialists utilizing Huawei infrastructure
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.
