{"type":"video","version":"1.0","html":"<iframe src=\"https://www.loom.com/embed/6c1a8bb072b6423098c6ebd5c28e6dec\" frameborder=\"0\" width=\"1920\" height=\"1440\" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>","height":1440,"width":1920,"provider_name":"Loom","provider_url":"https://www.loom.com","thumbnail_height":1440,"thumbnail_width":1920,"thumbnail_url":"https://cdn.loom.com/sessions/thumbnails/6c1a8bb072b6423098c6ebd5c28e6dec-dd7d0581c4c4c351.gif","duration":300.835,"title":"Building a Custom Machine Learning Solution for Sensitive Information Detection","description":"Hey everyone, GP here! In this video, I'm sharing my work as a privacy engineer focused on building a system to detect sensitive information in documents, like credit card names. I discuss the lack of features for creating custom machine learning entities, specifically neuro entities for named entity recognition. I also demonstrate a tool for adding training examples and generating synthetic data to improve our model. I would love to get your feedback on this approach, especially if you have ideas on how to make it more business-oriented."}