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Course Outline
Overview of Huawei CloudMatrix
- The CloudMatrix ecosystem and deployment workflow
- Compatible models, formats, and deployment types
- Common use cases and supported chipsets
Preparing Models for Deployment
- Exporting models from training tools (MindSpore, TensorFlow, PyTorch)
- Utilizing ATC (Ascend Tensor Compiler) for format adaptation
- Static versus dynamic shape models
Deploying on CloudMatrix
- Creating services and registering models
- Launching inference services through the UI or CLI
- Managing routing, authentication, and access controls
Handling Inference Requests
- Batch versus real-time inference workflows
- Preprocessing and postprocessing data pipelines
- Interfacing with CloudMatrix services from external applications
Monitoring and Performance Optimization
- Tracking deployment logs and requests
- Managing resource scaling and load balancing
- Adjusting latency and optimizing throughput
Enterprise Tool Integration
- Connecting CloudMatrix with OBS and ModelArts
- Implementing workflows and model version control
- CI/CD practices for model deployment and rollback
End-to-End Inference Pipeline
- Deploying a full image classification pipeline
- Benchmarking and validating accuracy metrics
- Simulating failover scenarios and system alerts
Wrap-up and Future Steps
Requirements
- Familiarity with AI model training processes
- Practical experience with Python-based machine learning frameworks
- Foundational knowledge of cloud deployment principles
Target Audience
- AI operations (MLOps) 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.