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Course Outline
Introduction to Huawei CloudMatrix
- The CloudMatrix ecosystem and deployment workflow
- Compatible models, formats, and deployment modes
- Common use cases and supported chipsets
Model Preparation for Deployment
- Exporting models from training tools such as MindSpore, TensorFlow, and PyTorch
- Employing ATC (Ascend Tensor Compiler) for format conversion
- Handling models with static versus dynamic shapes
Deployment on CloudMatrix
- Creating services and registering models
- Deploying inference services using the UI or CLI
- Managing routing, authentication, and access control
Handling Inference Requests
- Distinguishing between batch and real-time inference flows
- Implementing data preprocessing and postprocessing pipelines
- Integrating CloudMatrix services with external applications
Monitoring and Performance Optimization
- Analyzing deployment logs and tracking requests
- Managing resource scaling and load balancing
- Optimizing latency and throughput
Enterprise Tool Integration
- Connecting CloudMatrix with OBS and ModelArts
- Utilizing workflows and model versioning
- Implementing CI/CD for model deployment and rollback
Complete Inference Pipeline
- Deploying a full image classification pipeline
- Conducting benchmarks and validating accuracy
- Simulating failover scenarios and system alerts
Conclusion and Future Steps
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 experts 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.