Looking for Guidance on Deploying AI Visual Inspection Pipeline with NVIDIA Stack

AI & Data Science Computer Vision & Image Processing
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Hello NVIDIA Community,

I’m looking for guidance on deploying an AI-based visual inspection system into a production environment.

We have already built and tested several computer vision demos (object detection and defect detection) using deep learning models. The demos work well in controlled test scenarios, but we are now focusing on best practices for real-world deployment and scaling.

Specifically, I would appreciate advice on:

  1. Recommended NVIDIA tools or SDKs for deploying visual inspection systems (e.g., TensorRT, DeepStream, NIM, Triton).
  2. Best practices for optimizing inference performance on NVIDIA GPUs.
  3. Suggestions for handling real-time video streams and edge vs. cloud deployment.
  4. Any reference architectures, sample projects, or documentation relevant to industrial visual inspection.

Our goal is to move from demo-level validation to a reliable, production-ready solution using NVIDIA technologies.

Thank you in advance for any guidance or pointers.

AI_Demo_Overview.pdf (2.1 KB)