Artificial intelligence and machine learning get all the glory, but behind every great model is a great dataset and great data engineers to wrangle that data into shape. Data engineers get hired in a nearly two to one ratio at companies, but they often go unheralded. Their work is less glamorous, but without ingesting, cleaning, QAing, testing and modifying data so that it’s free of mislabeled data, redundancies, and dozens of other errors, there is no AI/ML. In the past, much of that data work fell to ML engineers themselves, but as organizations have grown, they’ve increasingly split the tasks into dedicated teams that focus on data and focus on algorithms. In this episode, hosts Mike Vizard and Lee Baker are joined by Shirley Wu (Juniper), Gaurav Pathak (Informatica) and Gopi Kokkonda (Alignity) to talk about the way of the data engineer and why it makes all the difference to your models in production.