LMT LAB DAY Chicago 2023

PyData Global 22 - Embeddings and Gradient Boosting in the Computer-Aided Manufacturing Industry

Utilizing Word Embeddings and Gradient Boosting to identify, analyze, prevent and predict machine errors in the Computer Aided Manufacturing Industry In this proposal, we talk about how we utilized data science and machine learning to analyze, prevent and predict system errors in a dental milling device....[More] The total market size estimated for the dental milling industry is close to $3billion+. Based on estimates by the World Health Organization, oral diseases affect close to 3.5 billion people worldwide. Often times these machines have a high cost of manufacturing and the unfavorable reimbursement policy hinders the customer experience. In this scenario, using data science/machine learning/artificial intelligence for predictive maintenance is absolutely critical. Based on years of data accumulated, we built systems that are capable of analyzing, preventing, and predicting system errors commonly found in a dental lab milling workflow. We show how we utilized state-of-the-art Natural Language Processing techniques and Classification algorithms to build a system that is able to analyze, prevent and predict system errors. We show how we efficiently applied data science techniques to aid in value generation and how we utilized state-of-the-art techniques in a manner that was applicable to a business problem. This talk is suitable for the general data science community and particularly data scientists/machine learning engineers interested to see how data science can efficiently be applied in a Computed Aided Manufacturing industry to yield favorable business outcomes. Source: https://www.youtube.com/watch?v=qqUGGBKtx0c&ab_channel=PydataMedia