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 CV/NLP Deployment with CANN
- The lifecycle of AI models from training through to deployment
- Critical performance factors for real-time CV and NLP applications
- An overview of CANN SDK tools and their significance in model integration
Preparing CV and NLP Models
- Exporting models from PyTorch, TensorFlow, and MindSpore environments
- Managing model inputs and outputs for image and text-based tasks
- Utilizing ATC to transform models into the OM format
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 using CANN utilities
- Minimizing latency through mixed-precision and batch tuning strategies
- Optimizing memory and compute resource usage for streaming tasks
Computer Vision Use Cases
- Case study: Implementing object detection for smart surveillance
- Case study: Visual quality inspection in manufacturing processes
- Developing live video analytics pipelines on Ascend 310 hardware
NLP Use Cases
- Case study: Sentiment analysis and intent detection systems
- Case study: Document classification and automated summarization
- Integrating real-time NLP with REST APIs and messaging platforms
Summary and Future Steps
Requirements
- Proficiency in deep learning principles for computer vision or NLP
- Practical experience with Python and AI frameworks like TensorFlow, PyTorch, or MindSpore
- Foundational knowledge of model deployment and inference workflows
Target Audience
- Computer vision and NLP professionals working with Huawei’s Ascend platform
- Data scientists and AI engineers creating real-time perception models
- Developers integrating CANN pipelines into sectors such as manufacturing, surveillance, or media analytics
