AI Inference and Deployment with CloudMatrix Training Course
CloudMatrix represents Huawei’s comprehensive unified platform for AI development and deployment, engineered to facilitate scalable, production-ready inference pipelines.
This instructor-led training, available either online or on-site, targets AI professionals at beginner to intermediate levels who aim to deploy and monitor AI models leveraging the CloudMatrix platform alongside CANN and MindSpore integration.
Upon completion of this training, participants will be capable of:
- Utilizing CloudMatrix for model packaging, deployment, and serving.
- Converting and optimizing models specifically for Ascend chipsets.
- Establishing pipelines for both real-time and batch inference tasks.
- Monitoring deployments and optimizing performance within production environments.
Course Format
- Interactive lectures accompanied by discussions.
- Practical application of CloudMatrix through real-world deployment scenarios.
- Guided exercises emphasizing conversion, optimization, and scaling.
Course Customization Options
- For customized training tailored to your specific AI infrastructure or cloud environment, please contact us to arrange.
Course Outline
Introduction to Huawei CloudMatrix
- CloudMatrix ecosystem and deployment flow
- Supported models, formats, and deployment modes
- Typical use cases and supported chipsets
Preparing Models for Deployment
- Model export from training tools (MindSpore, TensorFlow, PyTorch)
- Using ATC (Ascend Tensor Compiler) for format conversion
- Static vs dynamic shape models
Deploying to CloudMatrix
- Service creation and model registration
- Deploying inference services via UI or CLI
- Routing, authentication, and access control
Serving Inference Requests
- Batch vs real-time inference flows
- Data preprocessing and postprocessing pipelines
- Calling CloudMatrix services from external apps
Monitoring and Performance Tuning
- Deployment logs and request tracking
- Resource scaling and load balancing
- Latency tuning and throughput optimization
Integration with Enterprise Tools
- Connecting CloudMatrix with OBS and ModelArts
- Using workflows and model versioning
- CI/CD for model deployment and rollback
End-to-End Inference Pipeline
- Deploying a complete image classification pipeline
- Benchmarking and validating accuracy
- Simulating failover and system alerts
Summary and Next Steps
Requirements
- An understanding of AI model training workflows
- Experience with Python-based ML frameworks
- Basic familiarity with cloud deployment concepts
Audience
- AI ops teams
- Machine learning engineers
- Cloud deployment specialists working with Huawei infrastructure
Open Training Courses require 5+ participants.
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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.
Ireneusz - Inter Cars S.A.
Course - Intelligent Applications Fundamentals
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