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AWS Solutions Library

Guidance for Intelligent Yard Management on AWS

Overview

This Guidance demonstrates how to use annotated data to train machine learning (ML) models that help with transportation and logistics yard management. Transportation and logistics yards are complex environments that involve time-consuming manual tracking and monitoring activities. This architecture uses data from these environments to generate ML-powered insights to improve yard management.

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.

This architecture displays information on working statistics through services that allow for quick feedback, recovery, and refactoring.

Read the Operational Excellence whitepaper 

Data is encrypted in transit and at rest. The architecture uses the principle of least privilege and enforced login to protect access to data. Each layer of the application is secured and  monitored for traceability.

Read the Security whitepaper 

The architecture distributes workloads to avoid a single point of failure. It uses tracking key performance indicators (KPIs) to monitor reliability for production workloads. 

Read the Reliability whitepaper 

The architecture uses serverless functions (such as AWS Lambda ) where possible in addition to a data driven approach.

Read the Performance Efficiency whitepaper 

Customers pay only for resources used. Monitoring services check that applications are using resources efficiently so that customers do not pay for more resources than they actually need. 

Read the Cost Optimization whitepaper 

Serverless and managed services match instance size and usage to avoid wasted compute power. This architecture maximizes energy efficiency by adopting and aligning to a mature deployment approach using key AWS services, such as SageMaker , Amazon EC2 , and AWS Glue.

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.

References to third-party services or organizations in this Guidance do not imply an endorsement, sponsorship, or affiliation between Amazon or AWS and the third party. Guidance from AWS is a technical starting point, and you can customize your integration with third-party services when you deploy the architecture.