As its ML demands grew, EagleView encountered challenges with large workloads where the system had to handle thousands of requests at once. So, it sought a high-performance ML solution that could help manage its large pipelines for image data extraction.
Reported impact
The successful migration to Amazon SageMaker is the first step of the journey for EagleView to realize the full potential of the AWS suite of ML solutions. The team plans to catalog their model artifacts in a model registry and begin using tools like Amazon SageMaker Studio for end-to-end ML development. Key reported metrics include improvement in model performance 300–400%; 300–400%; reduction in compute costs 40–50%.
Learn how EagleView in the software industry optimized the performance of its data extraction models using Amazon SageMaker. EagleView needed a solution to handle its increasingly complex machine learning (ML) needs. So, it turned to Amazon Web Services (AWS) for a scalable solution that could improve system performance and reduce costs. EagleView migrated its data pipelines to Amazon SageMaker —where teams can build, train, and deploy ML models for any use case with fully managed infrastructure, tools, and workflows. As a result, it improved model performance by 300–400 percent, reduced compute costs by 40–50 percent, reduced processing time, met its customer service-level agreements (SLA) consistently, and improved overall system reliability. In 2021, the EagleView team started exploring using Amazon SageMaker and building a proof of concept.
Source data enters the EagleView workflow through its application services, processed with Amazon SageMaker, Amazon EKS, and delivered through Amazon SageMaker Asynchronous Inference.
300–400%
40–50%
As its ML demands grew, EagleView encountered challenges with large workloads where the system had to handle thousands of requests at once. So, it sought a high-performance ML solution that could help manage its large pipelines for image da
In 2021, the EagleView team started exploring using Amazon SageMaker and building a proof of concept.
The company migrated its two pipelines over to Amazon SageMaker within 8 months.
The successful migration to Amazon SageMaker is the first step of the journey for EagleView to realize the full potential of the AWS suite of ML solutions.
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