AI Inference and Deployment with CloudMatrix Training Course
CloudMatrix serves as Huawei’s consolidated platform for AI development and deployment, engineered to facilitate scalable, production-ready inference pipelines.
This instructor-led live training, available online or onsite, targets beginner to intermediate AI professionals seeking to deploy and monitor AI models using CloudMatrix, leveraging its integration with CANN and MindSpore.
Upon completion of this training, participants will gain the ability to:
- Utilize CloudMatrix for packaging, deploying, and serving models.
- Convert and optimize models specifically for Ascend chipsets.
- Establish pipelines for both real-time and batch inference tasks.
- Monitor deployments and optimize performance within production environments.
Course Format
- Interactive lectures and discussions.
- Practical application of CloudMatrix through real-world deployment scenarios.
- Guided exercises concentrating on conversion, optimization, and scaling techniques.
Course Customization Options
- To arrange a customized training session tailored to your specific AI infrastructure or cloud environment, please reach out to us.
Course Outline
Introduction to Huawei CloudMatrix
- Overview of the CloudMatrix ecosystem and deployment workflow.
- Supported models, file formats, and deployment modes.
- Common use cases and compatible chipsets.
Preparing Models for Deployment
- Exporting models from training tools such as MindSpore, TensorFlow, and PyTorch.
- Utilizing ATC (Ascend Tensor Compiler) for format conversion.
- Understanding static versus dynamic shape models.
Deploying to CloudMatrix
- Creating services and registering models.
- Deploying inference services through the UI or CLI.
- Managing routing, authentication, and access control.
Serving Inference Requests
- Comparing batch versus real-time inference flows.
- Implementing data preprocessing and postprocessing pipelines.
- Integrating CloudMatrix services with external applications.
Monitoring and Performance Tuning
- Analyzing deployment logs and tracking requests.
- Managing resource scaling and load balancing.
- Optimizing latency and throughput.
Integration with Enterprise Tools
- Connecting CloudMatrix with OBS and ModelArts.
- Utilizing workflows and managing model versioning.
- Implementing CI/CD for model deployment and rollback strategies.
End-to-End Inference Pipeline
- Deploying a complete image classification pipeline.
- Benchmarking and validating model accuracy.
- Simulating failover scenarios and system alerts.
Summary and Next Steps
Requirements
- A foundational understanding of AI model training workflows.
- Experience working with Python-based machine learning frameworks.
- Basic familiarity with cloud deployment concepts.
Target Audience
- AI operations teams.
- Machine learning engineers.
- Cloud deployment specialists utilizing 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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