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.
Architecture Diagram
Step 1
Recorded videos are used as input to create annotated data. The data is stored in a file system for later use.
Step 2
A computer vision (CV) model is trained in the ML training module using annotated data.
Step 3
In the ML training module, Amazon SageMaker infrastructure is critical for training custom CV models. The models are trained to detect physical assets using optical recognition.
Step 4
The trained model and business logic application is deployed to the AWS Panorama Appliance. Live internet protocol (IP) cameras send a feed to this appliance. Feeds are used for inference at the edge and are not recorded.
Step 5
The output from the AWS Panorama Appliance is used to create a web application hosted on an Amazon Elastic Compute Cloud (Amazon EC2) instance.
Step 6
End users access this web application through Amazon CloudFront. The web application presents a view of assets in the facility and a dashboard interface powered by Amazon QuickSight.
Step 7
Optional: Amazon Virtual Private Cloud (Amazon VPC) peering connects data to other external yard systems for enhanced functionality of asset tracking and monitoring.
Step 1
Recorded videos are used as input to create annotated data. The data is stored in a file system for later use.
Step 2
A computer vision (CV) model is trained in the ML training module using annotated data.
Step 3
In the ML training module, Amazon SageMaker infrastructure is critical for training custom CV models. The models are trained to detect physical assets using optical recognition.
Step 4
The trained model and business logic application is deployed to the AWS Panorama Appliance. Live internet protocol (IP) cameras send a feed to this appliance. Feeds are used for inference at the edge and are not recorded.
Step 5
The output from the AWS Panorama Appliance is used to create a web application hosted on an Amazon Elastic Compute Cloud (Amazon EC2) instance.
Step 6
End users access this web application through Amazon CloudFront. The web application presents a view of assets in the facility and a dashboard interface powered by Amazon QuickSight.
Step 7
Optional: Amazon Virtual Private Cloud (Amazon VPC) peering connects data to other external yard systems for enhanced functionality of asset tracking and monitoring.
Well-Architected Pillars
The AWS Well-Architected Framework helps you understand the pros and cons of the decisions you make when building systems in the cloud. The six pillars of the Framework allow you to learn architectural best practices for designing and operating reliable, secure, efficient, cost-effective, and sustainable systems. Using the AWS Well-Architected Tool, available at no charge in the AWS Management Console, you can review your workloads against these best practices by answering a set of questions for each pillar.
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.
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Operational ExcellenceRead the Operational Excellence whitepaper
This architecture displays information on working statistics through services that allow for quick feedback, recovery, and refactoring.
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SecurityRead the Security 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.
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ReliabilityRead the Reliability 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.
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Performance EfficiencyRead the Performance Efficiency whitepaper
The architecture uses serverless functions (such as AWS Lambda) where possible in addition to a data driven approach.
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Cost OptimizationRead the Cost Optimization 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.
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SustainabilityRead the Sustainability 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.
Disclaimer
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