Research Machine learning 2.0 - Engineering data driven AI products A paradigm shift from the current practice of creating machine learning models.
Engineering Feature Engineering vs Feature Selection All machine learning workflows depend on feature engineering and feature selection. However, they are often erroneously equated by the data science and machine learning communities. Although they share some overlap, these two ideas have different objectives. Knowing these distinct goals can tremendously improve your data science workflow and pipelines. This
Use Cases Applying Data Science Automation to Better Predict Credit Card Fraud An industry report from 2015 found that one out of every six legitimate cardholders experienced at least one declined transaction because of inaccurate fraud detection in the past year.
Research Towards increasing data scientists’ productivity by 1000x How to make data scientists more efficient
