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Recently, I had the chance to sit down with Rob and Lee at the InsureTech Podcast. It turned out to be three, relatively nerdy-tech guys, talking about data science and applied machine learning. Although we were wearing headsets and using microphones, I quickly forgot the equipment and the
three of us fell effortlessly into the conversation.

They wanted to know how Arturo started, what we are up to now and how we are helping insurers understand property characteristics by using tools like Arturo’s Confidence Score. …

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Here at we are big supporters of the Open Source Software (OSS) community. While I currently serve as Chief Technology Officer of Arturo, I am proud to have financially supported a number of OSS efforts during my previous role at the National Geospatial-Intelligence Agency (NGA). Additionally, I had the great opportunity to participate in the Free and Open Source Software for Geospatial (FOSS4G) community and conferences, to serve on the original planning committee for WhereCamp 5280, and to act as a Charter Member of OSGeo. Participating in, supporting, and gaining new friends and colleagues within the OSS community has…

In this series of engineering-focused blog posts, members of Arturo’s engineering team will dissect the infrastructure backing Arturo’s on-demand property analytics API.

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A sample of Arturo’s property analytics features. Our API gives unprecedented access to the latest possible property analytics data from the most up-to-date imagery available. As our machine learning services take just seconds to make a feature prediction, we’re able to offer our entire feature set on-demand — responding on average in less than five seconds.

For this first post, I’ll discuss the three types of services that compose our service graph: Machine Learning (ML) Models, 3rd-Party Data Providers, and Feature Aggregators.

Layers of a solution


Business decisions depend more on data than ever before, yet there is no way to know the reliability of each data point used in a decision. The best option for determining the reliability of data is to validate a subset of a data source against some (often arduously) collected ground truth. Not only is this process time consuming (in some cases impossible), but it is also ineffective due to data drift (the data used to gather the model performance statistics is somehow different than the data that is used for the decision making process).

Furthermore, this process only gives you…

The saying goes, “It takes a village to raise a child,” meaning that it takes an entire community interacting with a child for that child to safely/healthily grow and succeed in the community. Suppose if you will that roughly the same is true of the AI/ML community and the resulting models that are consumed. In order to provide the actual breadth of models and information needed by the world of potential users, it will take a village of providers to create the landscape of necessary capabilities. …


As a deep learning practitioner, it can be hard not to spend a great deal of time and energy tuning and re-tuning a model. What if I just change the learning rate a little? What if I alter the architecture to implement this cool new feature I read about today? There are near-infinite ways to tweak a model and squeeze out just a little more performance. However, this is often not the best or most efficient way to improve a model’s performance.

More often than not, the greatest obstacle to producing high-performance deep learning models is actually the data itself…

Meet Arturo.

Just a bit over 3 years ago, the Data Science and Analytics Lab (DSAL) inside American Family Insurance was asked to tackle two particularly tough questions…

  1. Could satellite, aerial, drone, and ground-level imagery combined with AI and Machine Learning substantially improve the quality and accuracy of property data used in quotation, underwriting, and renewal?
  2. If so, what would the impact be on the business and what ROI could be obtained?

Under the leadership of Dr. Martin Buchheim (who we are privileged to have as member of our Advisory Board), a team of Applied Machine Learning and Data Scientist…



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