How to Operationalize Machine Learning with Talend

Today's world has recently taken up an increased focus on machine learning and with data scientists/data miners/ predictive modellers / *whatever new job term may emerge* operating at the cutting-edge of technology, it cannot be forgotten that machine learning needs to be implemented in such a way to aid in the solution of real business problems. In day-to-day machine learning (ML) and the quest to deploy the knowledge gained, we typically encounter these three main problems (but not the only ones). The reason why these are important is that these issues affect the statistical properties of the datasets and interfere with the assumptions made by algorithms when run against these dirty data sets. For example, a customer churn model built with deep learning techniques might provide fantastic prediction accuracy but at the expense of interpretability and understanding how the model derived the answer. The business may have originally wanted a high accuracy model as well as an understanding into why customers churn.