Abstract: Similarity-based models have been extremely useful in scenarios when a typical classification model approach doesn’t work anymore. At Digits, we have developed a number of similarity-based machine learning models, whether to detect similar transactions or vendors. Those models are one of the capabilities that allow Digits to provide real-time insights to business owners and accountants. In this presentation, we are discussing the applications for similarity-based models at Digits, introducing the used frameworks and tools, as well as explaining how we implemented and evaluated such models. The team has a long history of developing similarity-based ML models, and we would like to share our lessons learned from the most recent implementations.
Bio: Hannes Hapke works in machine learning at Digits. Prior, he was a senior machine learning scientist for Concur Labs at SAP Concurfor Concur Labs at SAP Concur, where he explored innovative ways to use machine learning to improve the experience of a business traveler. Hannes has also solved machine learning and ML infrastructure problems in various industries including healthcare, retail, recruiting, and renewable energies. He was recognized as a Google Developer Expert for ML and has co-authored two machine learning publications: "Building Machine Learning Pipeline" by O'Reilly Media and "NLP in Action" by Manning Publications.