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Course Outline
Introduction to CV/NLP Deployment with CANN
- The AI model lifecycle, from training through to deployment
- Critical performance factors for real-time CV and NLP applications
- 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-based tasks
- Utilizing ATC to convert models into the OM format
Deploying Inference Pipelines with AscendCL
- Executing CV/NLP inference via the AscendCL API
- Preprocessing steps: image resizing, tokenization, and normalization
- Postprocessing: handling bounding boxes, classification scores, and text outputs
Performance Optimization Techniques
- Profiling CV and NLP models using CANN tools
- Reducing latency through mixed-precision computation and batch tuning
- Efficiently managing memory and compute resources for streaming tasks
Computer Vision Use Cases
- Case study: object detection for smart surveillance systems
- Case study: visual quality inspection in manufacturing
- Constructing live video analytics pipelines on Ascend 310
NLP Use Cases
- Case study: sentiment analysis and intent detection
- Case study: document classification and summarization
- Real-time NLP integration with REST APIs and messaging systems
Summary and Next Steps
Requirements
- Familiarity with deep learning techniques for computer vision or NLP
- Proficiency in Python and AI frameworks like TensorFlow, PyTorch, or MindSpore
- Basic knowledge of model deployment or inference workflows
Audience
- Practitioners in computer vision and NLP working with Huawei’s Ascend platform
- Data scientists and AI engineers developing real-time perception models
- Developers integrating CANN pipelines into manufacturing, surveillance, or media analytics systems
14 Hours
Testimonials (1)
I genuinely enjoyed the hands-on approach.
