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Course Outline
Introduction to Huawei CloudMatrix
- Overview of the CloudMatrix ecosystem and deployment workflows
- Compatible models, formats, and deployment strategies
- Common use cases and supported chipset types
Model Preparation for Deployment
- Exporting models from training tools (MindSpore, TensorFlow, PyTorch)
- Applying ATC (Ascend Tensor Compiler) for format adaptation
- Distinguishing between static and dynamic shape models
Deploying on CloudMatrix
- Creating services and registering models
- Rolling out inference services through the UI or command line
- Configuring routing, authentication, and access controls
Handling Inference Requests
- Comparing batch and real-time inference processes
- Implementing data preprocessing and postprocessing flows
- Invoking CloudMatrix services from external applications
Monitoring and Performance Optimization
- Analyzing deployment logs and tracking requests
- Managing resource scaling and load distribution
- Refining latency and maximizing throughput
Integration with Enterprise Solutions
- Linking CloudMatrix with OBS and ModelArts
- Leveraging workflows and model version management
- Implementing CI/CD for model deployment and rollback procedures
Complete Inference Pipeline Execution
- Deploying a full image classification workflow
- Conducting benchmarks and verifying accuracy
- Testing failover mechanisms and system alerts
Recap and Future Steps
Requirements
- Familiarity with AI model training processes
- Proficiency with Python-based machine learning frameworks
- Foundational knowledge of cloud deployment principles
Target Audience
- AI Operations teams
- Machine Learning Engineers
- Cloud deployment experts working within Huawei infrastructure environments
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.
