What challenges did EagleView face before AI implementation?

Large image-processing ML workloads were difficult to scale. The team struggled to meet near real-time SLAs during peaks of thousands of requests. Prior infrastructure required heavy configuration and debugging for large batch workloads.

What was the impact of the AI solution?

Model performance improved by 300-400%. Compute costs were reduced by 40-50%. Processing 1,000 square miles of aerial imagery dropped from 16 hours to 1.5 hours, a 90% reduction. The system met SLAs more consistently and improved overall reliability.

Rank#472of 3,107

Source

EagleView reduces costs and processing time for aerial imagery extraction with Amazon SageMaker

Use case typeMedical imagingUpdated Jun 13, 2026

EagleView uses aerial imagery and machine learning to provide insights for construction, real estate, insurance, emergency services, and energy customers. Its image-processing system must support large concurrent workloads and near real-time inference for use cases with tight SLAs. To address scaling and reliability challenges, EagleView migrated two ML pipelines from Amazon EKS-based infrastructure to Amazon SageMaker within eight months, standardizing deployment and using asynchronous inference and autoscaling to manage large request volumes.

Organization: EagleView
Industry: Real Estate
Location: United States
Published: May 2026

Reported outcomes

+400%

quantified impactOther quantified impact

−50%cost16 hourstime1.5 hourstime−90%time

Strategic outcomes

Cost efficiencyReduced compute costs for image processingSpeed & agilityAccelerated aerial imagery processingRisk & complianceMet SLAs more consistentlyScale & capacitySupported larger workloads with managed scaling

Primary read

Use case focus

Showing 3 of 3

1Machine Learning Inference
2Image Processing
3Workflow Automation

Large image-processing ML workloads were difficult to scale.
The team struggled to meet near real-time SLAs during peaks of thousands of requests.
Prior infrastructure required heavy configuration and debugging for large batch workloads.

EagleView migrated its pipelines to Amazon SageMaker for managed deployment and scaling.
The company used SageMaker Inference and SageMaker Asynchronous Inference to process requests efficiently and autoscale capacity to zero when idle.
EagleView streamlined model migration by using integrated NVIDIA Triton Inference Server containers on SageMaker.
The migration improved operational consistency and allowed the team to support larger workloads with less manual optimization.

Technologies

Amazon SageMaker Amazon SageMaker Inference Amazon SageMaker Asynchronous Inference Amazon EKS NVIDIA Triton Inference Server

Model performance improved by 300-400%.
Compute costs were reduced by 40-50%.
Processing 1,000 square miles of aerial imagery dropped from 16 hours to 1.5 hours, a 90% reduction.
The system met SLAs more consistently and improved overall reliability.

Architecture

EagleView migrated two ML pipelines from Amazon EKS to Amazon SageMaker. The deployment used SageMaker Inference, SageMaker Asynchronous Inference, autoscaling, and integrated NVIDIA Triton Inference Server containers to support large-scale image extraction workloads and near real-time inference.

Implementation partners1

NVIDIA

Sources & evidence1

Groundedness: 5/5

Primary source

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Customer evidence