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Course Outline

Introduction to CV/NLP Deployment with CANN

  • Understanding the AI model lifecycle from training to deployment.
  • Key performance considerations 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-based tasks.
  • Utilizing ATC to convert models into OM format.

Deploying Inference Pipelines with AscendCL

  • Executing CV/NLP inference via the AscendCL API.
  • Implementing preprocessing pipelines: image resizing, tokenization, and normalization.
  • Handling postprocessing: bounding boxes, classification scores, and text output.

Performance Optimization Techniques

  • Profiling CV and NLP models using CANN tools.
  • Reducing latency through mixed-precision processing and batch tuning.
  • Managing memory and compute resources for streaming tasks.

Computer Vision Use Cases

  • Case study: Object detection for smart surveillance systems.
  • Case study: Visual quality inspection in manufacturing environments.
  • Building live video analytics pipelines on Ascend 310 hardware.

Natural Language Processing (NLP) Use Cases

  • Case study: Sentiment analysis and intent detection.
  • Case study: Document classification and summarization.
  • Integrating real-time NLP with REST APIs and messaging systems.

Summary and Next Steps

Requirements

  • Familiarity with deep learning techniques for computer vision or NLP.
  • Experience using Python and AI frameworks such as TensorFlow, PyTorch, or MindSpore.
  • Basic understanding of model deployment and inference workflows.

Audience

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

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