AI Inference and Deployment with CloudMatrix Training Course
CloudMatrix serves as Huawei's unified platform for AI development and deployment, specifically designed to facilitate scalable, production-ready inference pipelines.
This live training session, which can be conducted online or at your premises under the guidance of an instructor, targets beginner to intermediate AI professionals. The objective is to equip participants with the skills to deploy and monitor AI models using the CloudMatrix platform, leveraging its integration with CANN and MindSpore.
Upon completing this training, participants will be capable of:
- Utilising CloudMatrix for the packaging, deployment, and serving of models.
- Converting and optimising models specifically for Ascend chipsets.
- Establishing pipelines for both real-time and batch inference tasks.
- Monitoring deployments and tuning performance within production environments.
Course Format
- Interactive lectures and group discussions.
- Practical application of CloudMatrix through real-world deployment scenarios.
- Guided exercises focusing on conversion, optimisation, and scaling.
Customization Options
- If you require a tailored version of this course based on your specific AI infrastructure or cloud environment, please contact us to make arrangements.
Course Outline
Introduction to Huawei CloudMatrix
- Overview of the CloudMatrix ecosystem and deployment workflow.
- Supported models, data formats, and deployment modes.
- Typical use cases and compatible chipsets.
Preparing Models for Deployment
- Exporting models from training tools such as MindSpore, TensorFlow, and PyTorch.
- Employing ATC (Ascend Tensor Compiler) for format conversion.
- Distinguishing between static and dynamic shape models.
Deploying to CloudMatrix
- Creating services and registering models.
- Deploying inference services via the user interface or command line interface (CLI).
- Managing routing, authentication, and access control.
Serving Inference Requests
- Differentiating between batch and real-time inference flows.
- Implementing data preprocessing and postprocessing pipelines.
- Integrating CloudMatrix services into external applications.
Monitoring and Performance Tuning
- Tracking deployment logs and requests.
- Managing resource scaling and load balancing.
- Optimising latency and throughput.
Integration with Enterprise Tools
- Connecting CloudMatrix with OBS and ModelArts.
- Utilising workflows and model versioning.
- Implementing CI/CD for model deployment and rollback procedures.
End-to-End Inference Pipeline
- Deploying a complete image classification pipeline.
- Benchmarking and validating accuracy.
- Simulating failover scenarios and system alerts.
Summary and Next Steps
Requirements
- A foundational understanding of AI model training workflows.
- Practical experience with Python-based machine learning frameworks.
- Basic familiarity with cloud deployment concepts.
Target Audience
- AI operations teams.
- Machine learning engineers.
- Cloud deployment specialists working with Huawei infrastructure.
Need help picking the right course?
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AI Inference and Deployment with CloudMatrix Training Course - Enquiry
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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