Normalized claim
Quantified impact: 300-400% increase
Model performance improved by 300-400%.
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
Impact: +300–400%
Other quantified impact
Cost: −40–50%Time: 16 hoursTime: 1.5 hoursTime: −90%
Why do we believe this?Outcome claims, sources, and evidence checks
Normalized claim
Cost: 40-50% decrease
Compute costs were reduced by 40-50%.
Normalized claim
Time: 16 hours decrease
Processing 1,000 square miles of aerial imagery dropped from 16 hours to 1.5 hours, a 90% reduction.
Normalized claim
Time: 1.5 hours decrease
Processing 1,000 square miles of aerial imagery dropped from 16 hours to 1.5 hours, a 90% reduction.
Normalized claim
Time: 90% decrease
Processing 1,000 square miles of aerial imagery dropped from 16 hours to 1.5 hours, a 90% reduction.
Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks
- Customer
- EagleView
- Provider
- AWS
- Maturity
- Production
- Linked source
- AWS Solutions Case Study
The migration improved operational consistency and allowed the team to support larger workloads with less manual optimization
Customer identity supportedSource describes one deploymentMaturity supported
Primary read
Use case focus
Showing 3 of 3
- 1Machine Learning Inference
- 2Image Processing
- 3Workflow Automation
- 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.
Model performance improved by 300-400%.
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
Sources & evidence1
Evidence: Medium65/100Evidence strength
- Customer explicitly identified
- Deployment status explicitly supported
- Primary source available
- Quantified outcome available
- Technical implementation details available
Type: Case StudyPublished: May 13, 2026Publisher: AWS Solutions Case StudyEvidence: PrimaryConfidence: High
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