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.
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.
- 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 to OM format.
Deploying Inference Pipelines with AscendCL
- Executing CV/NLP inference via the AscendCL API.
- Preprocessing pipelines: image resizing, tokenisation, and normalisation.
- Postprocessing: 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 foundational understanding of model deployment or inference workflows.
Target 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 in manufacturing, surveillance, or media analytics.
14 Hours
