Airbnb Engineering & Data Science
A machine learning library designed from the ground up to be human friendly. It is different from other machine learning libraries in the following ways: A thrift based feature representation that enables pairwise ranking loss and single context multiple item representation. A feature transform language gives the user a lot of control over the features Human friendly debuggable models Separate lig
Aerosolve
Example: Neighborhood polygons based on listing density in San Francisco, generated using a multi-scale Kd-tree model. Sophisticated ML Features Aerosolve provides sophisticated machine learning features, such as geo-based features, controllable quantization and feature interaction. Provide human intuition to machine models by specifying prior beliefs. Human Friendly, Debuggable Models Aerosolve w
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