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Course Outline
Introduction to Huawei CloudMatrix
- Overview of the CloudMatrix ecosystem and deployment architecture.
- Compatible models, formats, and deployment methodologies.
- Common use cases and supported chipset configurations.
Model Preparation for Deployment
- Exporting models from training environments (MindSpore, TensorFlow, PyTorch).
- Utilizing ATC (Ascend Tensor Compiler) for format conversion.
- Distinguishing between static and dynamic shape models.
Deployment to CloudMatrix
- Creating services and registering models.
- Deploying inference services via the UI or command-line interface.
- Configuring routing, authentication, and access control.
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
- Reviewing deployment logs and tracking requests.
- Managing resource scaling and load balancing.
- Tuning latency and optimizing throughput.
Enterprise Tool Integration
- Linking CloudMatrix with OBS and ModelArts.
- Utilizing workflows and model versioning strategies.
- 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.
Summary and Future Steps
Requirements
- Proficiency in AI model training workflows.
- Experience with Python-based machine learning frameworks.
- Foundational knowledge of cloud deployment principles.
Target Audience
- AI operations (AIops) 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.
