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Course Outline
Introduction to Huawei CloudMatrix
- Overview of the CloudMatrix ecosystem and its deployment workflow.
- Details on supported models, formats, and deployment modes.
- Common use cases and compatible chipsets.
Preparing Models for Deployment
- Exporting models from training tools such as MindSpore, TensorFlow, and PyTorch.
- Utilizing ATC (Ascend Tensor Compiler) for format conversion.
- Managing models with static versus dynamic shapes.
Deploying to CloudMatrix
- Creating services and registering models.
- Deploying inference services through the UI or CLI.
- Configuring routing, authentication, and access controls.
Serving Inference Requests
- Distinctions between batch and real-time inference flows.
- Implementing data preprocessing and postprocessing pipelines.
- Integrating CloudMatrix services with external applications.
Monitoring and Performance Tuning
- Analyzing deployment logs and tracking requests.
- Managing resource scaling and load balancing.
- Optimizing latency and throughput.
Integration with Enterprise Tools
- Linking CloudMatrix with OBS and ModelArts.
- Implementing workflows and model versioning strategies.
- Establishing CI/CD processes for model deployment and rollback.
End-to-End Inference Pipeline
- Deploying a comprehensive image classification pipeline.
- Benchmarking and verifying accuracy.
- Simulating failover scenarios and system alerts.
Summary and Next Steps
Requirements
- A solid grasp of AI model training workflows.
- Practical experience with Python-based machine learning frameworks.
- Foundational knowledge of cloud deployment concepts.
Target Audience
- AI operations 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.
