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Duration 21 hours (3 days)
Course Outline
Introduction to Huawei CloudMatrix
- The CloudMatrix ecosystem and deployment workflow
- Supported models, file formats, and deployment modes
- Common use cases and compatible chipsets
Model Preparation for Deployment
- Exporting models from training frameworks (MindSpore, TensorFlow, PyTorch)
- Employing ATC (Ascend Tensor Compiler) for format conversion
- Handling models with static versus dynamic shapes
Deploying to CloudMatrix
- Service creation and model registration procedures
- Deploying inference services via the UI or CLI
- Configuring routing, authentication, and access controls
Serving Inference Requests
- Comparing batch and real-time inference workflows
- Implementing data preprocessing and postprocessing pipelines
- Integrating CloudMatrix services with external applications
Monitoring and Performance Tuning
- Analysing deployment logs and tracking requests
- Managing resource scaling and load balancing
- Optimising latency and throughput
Enterprise Tool Integration
- Linking CloudMatrix with OBS and ModelArts
- Leveraging workflows and model versioning strategies
- Implementing CI/CD for model deployment and rollback
End-to-End Inference Pipeline
- Deploying a complete image classification pipeline
- Benchmarking and validating model accuracy
- Simulating failover scenarios and system alerts
Summary and Future Steps
Requirements
- A foundational grasp of AI model training workflows
- Familiarity with Python-based ML frameworks
- Basic knowledge of cloud deployment concepts
Intended Audience
- AI operations (AIOps) teams
- Machine learning engineers
- Cloud deployment specialists utilising Huawei infrastructure
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.
