ML Data Management -- A Primer

A machine learning (ML) model's performance is determined by code and data. When trying to improve a ML model you can write better code, increase testing, or improve the data itself. The ML space is maturing with more companies pushing models to production than ever before. With this shift, teams are less challenged by how to build and deploy a model, but rather on improving a model's precision and recall, which often means iterating on the training data. Data has notoriously been a constraint to building great models and has led to the rise of data labeling providers like Scale.