Implementing critical machine learning-based business applications, from Know Your Customer (KYC), through to Anti-money Laundering (AML) and Fraud Detection, is difficult when data originates from many different sources and geographies, is incorrect, incomplete or badly formatted.
Machine Learning models need large volumes of reliable, current, clean data to do their jobs. Building data cleansing and entity resolution processes on Big Data platforms like Hadoop and Spark is no picnic – and when models get put into practice, the data quickly becomes out of date. So, how do you keep the data in your cluster reliably in sync with transactional source systems in production?
Join us to find out how the key challenges of enabling Machine Learning, in even some of the toughest use cases, can be tackled with real-time data integration and data quality at scale.