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Course Outline
Overview of Huawei CloudMatrix
- The CloudMatrix ecosystem and its deployment lifecycle
- Compatible models, data formats, and deployment modes
- Common use cases and supported chipset architectures
Model Preparation for Deployment
- Exporting models from training frameworks (MindSpore, TensorFlow, PyTorch)
- Utilizing the Ascend Tensor Compiler (ATC) for format conversion
- Handling static versus dynamic shape models
Deployment to CloudMatrix
- Creating services and registering models
- Launching inference services via the UI or command-line interface (CLI)
- Implementing routing, authentication, and access control mechanisms
Serving Inference Requests
- Differentiating between batch and real-time inference workflows
- Establishing 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 balancing
- Optimizing latency and maximizing throughput
Enterprise Tool Integration
- Connecting CloudMatrix with OBS and ModelArts
- Leveraging workflows and model versioning strategies
- Implementing CI/CD pipelines for model deployment and rollback
Complete Inference Pipeline
- Deploying a full image classification pipeline
- Conducting benchmarking and accuracy validation
- Simulating failover scenarios and system alerting
Recap and Future Directions
Requirements
- A solid grasp of AI model training workflows
- Proficiency 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.