10 Types of Features your Location ML Model is Missing // Anne Cocos // Coffee Sessions #58
MLOps.community
Are We Deprecating the Old API?
Pivoting from trying to service both camps, except neither one really was on board. Reaching out to all the data scientists and are nat work and trying to understand how they use spatial data. And then also sort of pivoting and focusing the team around building this first product.
Coffee Sessions #58 with Anne Cocos, 10 Types of Features your Location ML Model is Missing.
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// Abstract
Machine learning on geographic data is relatively understudied in comparison to ML on other formats like images or graphs. But geographic data is prevalent across a wide variety of domains (although many practitioners may not think of it that way). Clearly, any dataset with `latitude` and `longitude` columns can be viewed as geographic data, but also any dataset with a `zipcode`, `city`, `address`, or `county` can be construed as geographic. Demographics, weather, foot traffic, points of interest, and topographic features can all be used to enrich a dataset with any of these types of keys.
Incorporating relatively straightforward geographic features into models can yield substantial improvements; adding "distance to the beach" or "square mileage reachable within a 10-minute drive" to a real estate pricing model, for example, can lead to significant decreases in model error.
Unfortunately, many ML teams find it difficult to incorporate these types of geographic data into their models because the process of ingesting from geographic formats (geojson or shapefiles), projecting, and properly joining with their existing data can be a large infrastructure lift.
In this coffee session, Anne discusses ways to simplify the process of incorporating geographic or location data into the MLOps workflow, as well as interesting trends in the geographic ML research community that will ultimately make it easier for us to learn from geography just as we do with images or graphs today.
// Bio
Dr. Anne Cocos currently leads data science and machine learning at Ask Iggy, Inc., a venture-backed, seed round startup focused on location analytics. Her team builds tools that make it simple for data scientists to leverage location information in their models and analyses.
Previously, she was the Director and Head, NLP and Knowledge Graph at GlaxoSmithKline, where she built algorithms and infrastructure to enable GSK’s scientists to leverage all the world’s written biomedical knowledge for drug discovery. She also worked on applied natural language processing research at The Children’s Hospital of Philadelphia Department of Biomedical Informatics. Anne completed her Ph.D. in computer science at the University of Pennsylvania, where she was supported by the Google Ph.D. Fellowship and the Allen Institute for Artificial Intelligence Key Scientific Challenges award.
Before shifting her career toward artificial intelligence, Anne spent several years as an end-user of early ML-powered technologies in the U.S. Navy and at HelloWallet. Her previous degrees are from the U.S. Naval Academy, Royal Holloway University of London, and Oxford University. She currently lives just outside Philadelphia with her husband and three boys.
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