Real-time Analytics
Founded in 2008, Nearmap uses aerial camera technology and in-house vision and ML pipelines to help organizations track changes, such as urban growth, the effects of natural disasters, and changes in tree coverage.
Reported impact
By adopting Amazon SageMaker, Nearmap can access and analyze petabytes of information for AI and ML. The company's data is growing every day, and the underlying computer resources of Amazon SageMaker help Nearmap scale seamlessly to accommodate the demands of its business, team members, and customers.
With its aerial camera technology, Nearmap offers organizations a dynamic lens to track structural and environmental changes over time. Taking full advantage of over 50 PB of imagery data with custom deep learning models requires advanced and powerful technology. To support its growing machine learning (ML) training and artificial intelligence (AI) needs, Nearmap upgraded from on-premises hardware to robust and scalable solutions from Amazon Web Services (AWS). Large, custom deep learning models, and the current trend toward large vision models, require the ability to dynamically scale up training that uses multiple machines at once. Nearmap not only found a way to reduce the time to train an ML model through larger bursts of training compute but has also paved the way for even larger models that unlock the full value of its dataset, revolutionizing its AI capabilities.
"Using Amazon SageMaker, our model training process has become much more streamlined, which helps us perform ML at scale," says Dr. With Amazon SageMaker Model Training capabilities, the company no longer needs to perform this cumbersome process; Nearmap can train models directly on Amazon SageMaker using data stored in Amazon S3, leading to faster model training and deployment. Nearmap can also perform multiple model training tasks in parallel on Amazon SageMaker, which further accelerates the ML training process.
"On AWS, we're free from managing overlapping resources, which simplifies planning for project and model training," says Dr.
After exploring potential solutions, Nearmap set its sights on AWS.
However, Nearmap found that it required more flexibility and scalability to train its ML models on multiple nodes of A100 GPUs and reduce the need to plan for compute availability.
Nearmap adopted the AWS service Amazon SageMaker —a fully managed service that helps developers and data scientists build, train, and deploy ML models for nearly any use case—and uses Amazon Simple Storage Service (Amazon S3), an object...
Nearmap not only found a way to reduce the time to train an ML model through larger bursts of training compute but has also paved the way for even larger models that unlock the full value of its dataset, revolutionizing its AI capabilities.
To support its growing machine learning (ML) training and artificial intelligence (AI) needs, Nearmap upgraded from on-premises hardware to robust and scalable solutions from Amazon Web Services (AWS).
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The annual report says, "Nearmap employs almost 400 people globally," which supports a Mid-market size classification.
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