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Course Outline
Overview of Huawei CloudMatrix
- The CloudMatrix ecosystem and its deployment architecture
- Compatible models, file formats, and deployment modes
- Common use cases and supported chipset options
Model Preparation for Deployment
- Exporting models from training environments (MindSpore, TensorFlow, PyTorch)
- Utilizing ATC (Ascend Tensor Compiler) for format transformation
- Differentiating between static and dynamic shape models
Deploying on CloudMatrix
- Creating services and registering models
- Launching inference services through the UI or CLI
- Managing routing, authentication, and access controls
Handling Inference Requests
- Comparing batch and real-time inference workflows
- Implementing data preprocessing and postprocessing pipelines
- Integrating CloudMatrix services into external applications
Monitoring and Performance Optimization
- Analyzing deployment logs and tracking request activity
- Implementing resource scaling and load balancing strategies
- Refining latency and enhancing throughput
Enterprise Tool Integration
- Linking CloudMatrix with OBS and ModelArts
- Leveraging workflows and model versioning features
- Establishing CI/CD pipelines for deployment and rollback
Full-Stack Inference Pipeline
- Building a complete image classification pipeline
- Conducting benchmarking and accuracy validation
- Testing failover mechanisms and system alerting
Recap and Future Directions
Requirements
- A solid grasp of AI model training processes
- Proficiency with Python-based machine learning frameworks
- Foundational knowledge of cloud deployment principles
Target Audience
- AI Operations (AIOps) teams
- Machine Learning Engineers
- Cloud deployment experts utilizing Huawei infrastructure
21 Hours
Testimonials (1)
The trainer's open and warm style, and the accessible language.
OPREAN ADELA - Aeronamic Eastern Europe
Course - AI Enablement Training for Engineers
Machine Translated