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Course Outline

Introduction to CV/NLP Deployment with CANN

  • Understanding the AI model lifecycle from training to deployment
  • Key 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 tasks
  • Utilizing ATC to convert models into OM format

Deploying Inference Pipelines with AscendCL

  • Executing CV/NLP inference via the AscendCL API
  • Preprocessing pipelines: image resizing, tokenization, and normalization
  • Postprocessing: handling bounding boxes, classification scores, and text output

Performance Optimization 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 summarization
  • 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 like TensorFlow, PyTorch, or MindSpore
  • Basic understanding of model deployment or inference workflows

Audience

  • Practitioners working with Huawei’s Ascend platform for computer vision and NLP
  • Data scientists and AI engineers creating real-time perception models
  • Developers integrating CANN pipelines within manufacturing, surveillance, or media analytics sectors
 14 Hours

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