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Struttura del corso
Introduction to CV/NLP Deployment with CANN
- AI model lifecycle from training to deployment
- Key performance considerations for real-time CV and NLP
- Overview of CANN SDK tools and their role in model integration
Preparing CV and NLP Models
- Exporting models from PyTorch, TensorFlow, and MindSpore
- Handling model inputs/outputs for image and text tasks
- Using ATC to convert models to OM format
Deploying Inference Pipelines with AscendCL
- Running CV/NLP inference using the AscendCL API
- Preprocessing pipelines: image resizing, tokenization, normalization
- Postprocessing: bounding boxes, classification scores, text output
Performance Optimization Techniques
- Profiling CV and NLP models using CANN tools
- Reducing latency with mixed-precision and batch tuning
- Managing memory and compute 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 summarization
- Real-time NLP integration with REST APIs and messaging systems
Summary and Next Steps
Requisiti
- Familiarity with deep learning for computer vision or NLP
- Experience with Python and AI frameworks such as TensorFlow, PyTorch, or MindSpore
- Basic understanding of model deployment or inference workflows
Audience
- Computer vision and NLP practitioners using Huawei’s Ascend platform
- Data scientists and AI engineers developing real-time perception models
- Developers integrating CANN pipelines in manufacturing, surveillance, or media analytics
14 ore
Recensioni (1)
I genuinely enjoyed the hands-on approach.
