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Duration 14 hours
Course Outline
Introduction to CV/NLP Deployment with CANN
- The AI model lifecycle from training through to deployment
- Key performance considerations for real-time CV and NLP applications
- An overview of CANN SDK tools and their role in model integration
Preparing CV and NLP Models
- Exporting models from PyTorch, TensorFlow, and MindSpore
- Managing model inputs and outputs for image and text tasks
- Using ATC to convert models into OM format
Deploying Inference Pipelines with AscendCL
- Executing CV/NLP inference via the AscendCL API
- Preprocessing pipelines: image resizing, tokenisation, and normalisation
- Postprocessing: handling bounding boxes, classification scores, and text output
Performance Optimisation Techniques
- Profiling CV and NLP models using CANN tools
- Reducing latency through mixed-precision and batch tuning
- Managing memory and compute resources for streaming tasks
Computer Vision Use Cases
- Case study: object detection for smart surveillance
- Case study: visual quality inspection in manufacturing
- Building live video analytics pipelines on Ascend 310
NLP Use Cases
- Case study: sentiment analysis and intent detection
- Case study: document classification and summarisation
- Real-time NLP integration with REST APIs and messaging systems
Summary and Next Steps
Requirements
- Familiarity with deep learning concepts in computer vision or NLP
- Experience with Python and AI frameworks such as TensorFlow, PyTorch, or MindSpore
- A basic understanding of model deployment or inference workflows
Audience
- Computer vision and NLP practitioners utilising Huawei’s Ascend platform
- Data scientists and AI engineers developing real-time perception models
- Developers integrating CANN pipelines within manufacturing, surveillance, or media analytics sectors
