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Course Outline
Introduction to Huawei CloudMatrix
- Overview of the CloudMatrix ecosystem and deployment workflow
- Supported models, data formats, and deployment modes
- Common use cases and compatible chipsets
Preparing Models for Deployment
- Exporting models from training frameworks such as MindSpore, TensorFlow, and PyTorch
- Employing ATC (Ascend Tensor Compiler) for format conversion
- Distinctions between static and dynamic shape models
Deploying to CloudMatrix
- Creating services and registering models
- Deploying inference services via the user interface or CLI
- Configuring routing, authentication, and access control
Serving Inference Requests
- Differentiating between batch and real-time inference workflows
- Implementing data preprocessing and postprocessing pipelines
- Invoking CloudMatrix services from external applications
Monitoring and Performance Tuning
- Accessing deployment logs and tracking requests
- Managing resource scaling and load balancing
- Optimizing latency and enhancing throughput
Integration with Enterprise Tools
- Linking CloudMatrix with OBS and ModelArts
- Utilizing workflows and model versioning controls
- Implementing CI/CD practices for deployment and rollback procedures
End-to-End Inference Pipeline
- Deploying a comprehensive image classification pipeline
- Benchmarking and validating model accuracy
- Simulating failover scenarios and system alerts
Summary and Next Steps
Requirements
- Knowledge of AI model training workflows
- Proficiency with Python-based machine learning frameworks
- Fundamental understanding of cloud deployment principles
Audience
- AI operations teams
- Machine learning engineers
- Cloud deployment specialists working with 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.
