Data scientists are constantly challenged with improving their ML models. But when a new algorithm won’t improve your AUC there’s only one place to look: DATA. So, what do you do? Look at the limited internal data your organization has and pray you can squeeze something new out of it or look for external sources?
Explorium is the first company to extend the scope of autoML to include automated data discovery and feature engineering. Based on our extensive experience, we have distilled the data acquisition process down to six easy-to-follow steps and are sharing them with you: The Complete Guide to Data Acquisition for ML.