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Guidance for Cognizant's APEx Quality Management through Computer Vision on AWS

Overview

This Guidance helps you reduce the time required to set up an in-plant computer vision (CV) quality detection system. It provides workflows and interfaces that enable quality subject matter experts to train CV models so that you don’t have to procure expensive data scientists. The architecture includes a series of interfaces for one-touch gateway onboarding, which eliminates the need for specialized personnel to set up, deploy, and manage the gateways and CV models. The architecture also uses edge technology, machine learning (ML), and user interfaces to initiate quality inference, orchestrate gateway deployments, calculate key performance indicators (KPIs), and visualize data through dashboards.

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

Well-Architected Pillars

The architecture diagram above is an example of a Solution created with Well-Architected best practices in mind. To be fully Well-Architected, you should follow as many Well-Architected best practices as possible.

KPI and summary dashboards are a feature of this Guidance. The dashboard displays metrics such as OEE and the number of good and bad parts from the images. You can use this dashboard to examine your manufacturing operations and determine whether you are achieving expected outcomes.  

Read the Operational Excellence whitepaper

The edge-to-cloud communications use security mechanisms provided by AWS, including X.509 certificates for mutual transport layer security (mTLS) and bi-directional encryption of traffic. AWS Identity and Access Management (IAM) roles are used for deployment, and interservice communications are configured following the principle of least privilege access.  

Read the Security whitepaper

This Guidance uses managed services, which are highly available by design. The in-plant gateway is not highly available, but the Gateway Management feature provides one-touch gateway onboarding, enabling a low recovery time objective (RTO). You can configure what data to back up and where to store that data.

Read the Reliability whitepaper

Services in this Guidance were selected to reduce the undifferentiated heavy lifting for the end user while operating the solution. For example, AWS IoT SiteWise is used to collect, store, and process incoming data in near real time. This allows you to focus on KPIs important to your business rather than the underlying technology of ingesting data. AWS IoT SiteWise Edge enables the same business logic that operates in the cloud to be deployed to the edge device. This reduces efforts to maintain multiple disparate systems.  

Read the Performance Efficiency whitepaper

When choosing services for this Guidance, we considered the overall costs of maintaining the architecture, not just the costs for operating a service. For example, although AWS IoT SiteWise Edge has a significant monthly cost, this service will help you save on costs related to customer development, support, and maintenance.  

Read the Cost Optimization whitepaper

This Guidance uses a combination of managed services and services that elastically scale based on demand. As managed services, AWS IoT Core, AWS IoT SiteWise, and Amazon S3 provide compute and storage on demand. Amazon EKS is configured to scale up and down based on usage.  

Read the Sustainability whitepaper

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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