Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Duration 14 hours
Course Outline
Introduction to Deploying CV/NLP with CANN
- The AI model lifecycle: from training through to deployment
- Critical performance factors for real-time CV and NLP applications
- An overview of CANN SDK tools and their function in model integration
Preparing CV and NLP Models
- Exporting models from PyTorch, TensorFlow, and MindSpore
- Managing model inputs/outputs for image and text tasks
- Converting models to OM format using ATC
Deploying Inference Pipelines with AscendCL
- Executing CV/NLP inference via the AscendCL API
- Preprocessing workflows: image resizing, tokenization, and normalization
- Postprocessing steps: extracting bounding boxes, classification scores, and text outputs
Performance Optimization Techniques
- Profiling CV and NLP models with CANN tools
- Lowering latency through mixed-precision and batch tuning
- Optimizing memory and compute resources for streaming tasks
Computer Vision Use Cases
- Case study: Object detection for intelligent surveillance
- Case study: Visual quality inspection in manufacturing processes
- Developing 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 via REST APIs and messaging systems
Summary and Next Steps
Requirements
- Proficiency in deep learning techniques for computer vision or NLP
- Practical experience with Python and major AI frameworks like TensorFlow, PyTorch, or MindSpore
- A foundational grasp of model deployment and inference workflows
Target Audience
- Computer vision and NLP specialists working with Huawei’s Ascend platform
- Data scientists and AI engineers creating real-time perception models
- Developers implementing CANN pipelines in sectors such as manufacturing, surveillance, or media analytics
