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  • Guidance for Implementing AI-Powered Visual Quality Management with SoftServe EdgeInsight on AWS

Guidance for Implementing AI-Powered Visual Quality Management with SoftServe EdgeInsight on AWS

Overview

This Guidance demonstrates how to implement advanced industrial quality management using AI-powered computer vision at the edge with the EdgeInsight solution accelerator on AWS. It helps manufacturers rapidly deploy sophisticated monitoring systems that process video streams in near real-time using NVIDIA-accelerated machine learning. Organizations can benefit from local processing of camera feeds, seamless integration with existing factory OT data sources, and flexible data routing to either AWS cloud services or on-premise databases. This Guidance enables quick deployment of intelligent quality control systems while maintaining operational efficiency and reducing latency through edge processing.

Benefits

Deploy AI-powered computer vision applications at the edge in less time with standardized interfaces for diverse camera systems. You can rapidly implement quality inspection and monitoring solutions without managing complex hardware protocols or vendor-specific specifications.

Correlate visual data with operational technology inputs to gain comprehensive manufacturing insights. By combining video analytics with data from PLCs, DCS, and SCADA systems, you can identify root causes of quality issues and optimize production processes.

Implement a complete edge-to-cloud ML lifecycle that evolves with your manufacturing needs. Your models can be continuously trained with real and synthetic data in SageMaker, then securely deployed to edge devices through automated MLOps pipelines.

How it works

These technical details feature an architecture diagram to illustrate how to effectively use this solution. The architecture diagram shows the key components and their interactions, providing an overview of the architecture's structure and functionality step-by-step.

Download the architecture diagram

Disclaimer

The sample code; software libraries; command line tools; proofs of concept; templates; or other related technology (including any of the foregoing that are provided by our personnel) is provided to you as AWS Content under the AWS Customer Agreement, or the relevant written agreement between you and AWS (whichever applies). You should not use this AWS Content in your production accounts, or on production or other critical data. You are responsible for testing, securing, and optimizing the AWS Content, such as sample code, as appropriate for production grade use based on your specific quality control practices and standards. Deploying AWS Content may incur AWS charges for creating or using AWS chargeable resources, such as running Amazon EC2 instances or using Amazon S3 storage.

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