What's ETL? - KDnuggets

In my last post, I talked about what it means to move machine learning (ML) models into production by introducing the concept of MLOps. This time we're going to look at the opposite end of the data science steps for ML -- data extraction and integration. ETL stands for Extract-Transform-Load, it usually involves moving data from one or more sources, making some changes, and then loading it into a new single destination. Most ML algorithms require large amounts of training data in order to produce models that can make accurate predictions. They also require good quality training data, representative of the problem we are trying to solve.