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Course Outline
Getting Started with Huawei CloudMatrix
- The CloudMatrix ecosystem and its deployment architecture
- Compatible models, file formats, and deployment strategies
- Common use cases and supported hardware chipsets
Readying Models for Deployment
- Exporting models from training environments (MindSpore, TensorFlow, PyTorch)
- Leveraging ATC (Ascend Tensor Compiler) for format transformation
- Differentiating between static and dynamic shape models
Deployment on CloudMatrix
- Creating services and registering models
- Launching inference services via the interface or command line
- Managing routing, authentication, and access permissions
Handling Inference Requests
- Distinguishing between batch and real-time inference processes
- Implementing data preprocessing and postprocessing workflows
- Interacting with CloudMatrix services from external applications
Monitoring and Performance Adjustment
- Reviewing deployment logs and tracking requests
- Implementing resource scaling and load balancing
- Optimizing latency and throughput
Enterprise Tool Integration
- Linking CloudMatrix with OBS and ModelArts
- Employing workflows and managing model versions
- Implementing CI/CD for model deployment and rollback capabilities
Complete Inference Pipeline
- Deploying a full image classification pipeline
- Benchmarking performance and verifying accuracy
- Testing failover mechanisms and system alerts
Recap and Future Actions
Requirements
- Familiarity with AI model training processes
- Practical experience with Python-based machine learning frameworks
- Fundamental knowledge of cloud deployment principles
Target Audience
- AI operations teams
- Machine learning engineers
- Cloud deployment specialists operating within Huawei’s 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.