Software Engineering
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Serverless Devise with Jets
Over the past few years, serverless design has taken the cloud community by storm. It is hard to ignore–with promises like “pay only for what you use”, “no security patching”, and “infinite scalability”. Being on the cutting edge can have drawbacks too, which in this case is what I would describe as an absence of…
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Serverless Devise with Jets
Over the past few years, serverless design has taken the cloud community by storm. It is hard to ignore–with promises like “pay only for what you use”, “no security patching”, and “infinite scalability”. Being on the cutting edge can have drawbacks too, which in this case is what I would describe as an absence of…
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Partnering with CloudFactory on Quality Training Data and Social Impact
Read about our journey to hiring an outsourced data labeling firm and how we’ve found a partner in CloudFactory.
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STAC: Creating an Ecosystem of SpatioTemporal Assets
STAC is creating an ecosystem of interoperable spatiotemporal assets. Learn how Azavea has contibuted and about future steps for the specification.
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Transfer Learning from RGB to Multi-band Imagery
We used transfer learning to teach a model to take advantage of multi-band imagery without discarding the original RGB pre-training. This resulted in significant performance improvement.
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Using the Dedupe Machine Learning Library for Cleaning and Matching Data
To deal with issues of apparel facility list data quality and scale quickly and efficiently we need a machine learning tool that can capture the knowledge of domain experts, find commonalities in jumbled text, and confidently compare large lists without the need to compare each individual entry.
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Rising From The Ashes, Untangling the Mysteries of DevOps
There are many books that have influenced me as a software engineer. From the computer science textbooks I used in college, to the animal covered books from O’Reilly I used to pick up new languages and tools, to the Clean Code books from Uncle Bob Martin that helped shape the way I approach software development. However,…
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Connecting Data and People with the Open Apparel Registry
Since its launch on March 28, the Open Apparel Registry (OAR) has grown to include over 18,300 facilities in 92 countries. We’ve already heard of a few fascinating use cases where data from the OAR contributed to decision making by brands and facilities.
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Using Noisy Labels to Train Deep Learning Models on Satellite Imagery
How do noisy labels affect the accuracy of a deep learning model? We added different amounts of noise to the SpaceNet Vegas buildings dataset and trained some models to find out.
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An Introduction to Machine Learning Accuracy Metrics
How accurate of a machine learning model can you make? It depends on how we decide to define “accurate”.
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E84 @ 2019 ESIP Summer Meeting
We’re passionate about Earth Science Data and helping to solve the challenges in that field. At this year’s ESIP Summer Meeting, we have five E84 team members speaking and moderating across six different sessions, with an additional four of us out there to chat and support the event. Here’s where you can find us if…