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Course Outline
Overview of Huawei CloudMatrix
- The CloudMatrix ecosystem and deployment architecture
- Compatible models, file formats, and deployment strategies
- Common applications and supported chipset types
Model Preparation for Deployment
- Exporting models from training frameworks (MindSpore, TensorFlow, PyTorch)
- Employing ATC (Ascend Tensor Compiler) for format transformation
- Distinguishing between static and dynamic shape models
Deployment on CloudMatrix
- Creating services and registering models
- Launching inference services via the User Interface or Command Line Interface
- Configuring routing, authentication, and access permissions
Handling Inference Requests
- Comparing batch and real-time inference processes
- 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 distribution
- Adjusting latency and enhancing throughput
Integration with Enterprise Solutions
- Linking CloudMatrix with OBS and ModelArts
- Leveraging workflows and model version control
- Implementing CI/CD for model deployment and rollback processes
Complete Inference Pipeline
- Deploying a full image classification pipeline
- Conducting benchmarks and validating accuracy
- Simulating failover scenarios and system alerts
Conclusion and Future Directions
Requirements
- Familiarity with AI model training workflows
- Proficiency 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.