{"id":12831,"date":"2025-02-12T15:02:48","date_gmt":"2025-02-12T23:02:48","guid":{"rendered":"https:\/\/live-cometml.pantheonsite.io\/?page_id=12831"},"modified":"2025-06-12T17:44:41","modified_gmt":"2025-06-12T17:44:41","slug":"melanie-riley","status":"publish","type":"page","link":"https:\/\/www.comet.com\/site\/convergence\/melanie-riley\/","title":{"rendered":"Melanie Riley"},"content":{"rendered":"\n
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\u2190 BACK TO EVENT PAGE<\/a><\/p>\n\n\n\n

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\"Melanie<\/figure>\n<\/div>\n\n\n\n
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Melanie Riley<\/h1>\n\n\n

Machine Learning Engineer at Fetch<\/h2>\n<\/div>\n\n\n\n
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May 8, 2024 @ 1:10 \u2013 1:40 pm ET<\/p>\n\n\n\n

Optimizing Sentence Transformers for Entity Resolution at Scale<\/h2>\n\n\n\n
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At Fetch, we reward our users for snapping pictures of their receipts. Each day, this happens over 11 million times. Our machine learning and engineering teams are hard at work building systems that extract, normalize, and enrich information from these receipts as accurately and quickly as possible. One of the most important steps in this process is entity resolution. Entity resolution is the process of identifying and linking records that correspond to the same entity across different data sources. Paper receipts have diverse conventions for representing important text entities such as the originating business or \u201cretailer\u201d, purchased product descriptions, and payment information. In this talk, we will cover conception to deployment and discuss how we adapt popular sentence transformers and approximate nearest neighbor algorithms to our unique domain of receipt-language. We will discuss how we optimized the models for real-time production workloads and deployed the models to our 18 million MAU. Today the system makes inferences on over 11 million receipts uploaded each day. We also use Comet ML to track our model experiments<\/p>\n\n\n\n

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