The Importance of Data Engineering in Machine Learning 

When we think of machine learning (ML), data engineering may not be the first discipline that comes to mind—more likely, it’s data science. However, both data science and data engineering are integral to ML success. At a high level, data science creates methods of monetizing data for the organization (in this context by way of ML models). Data engineering, on the other hand, is a discipline of building and maintaining data-based systems. The work of data engineering ensures that data is harvested, inspected for quality, and readily accessible by appropriate data professionals throughout the organization. 

Without the data that data engineering efforts provide, data scientists could not build ML models. Even further, robust ML models demand huge volumes of training data so that the models are exposed to and learn from as many scenarios as possible. Data engineering teams are responsible for ensuring that data scientists have access to the data they need—and that this data is relevant, timely, and high-quality. Data engineering is especially critical when a company seeks to move its data science project into production, standardizing the early exploratory work of data scientists into pipelines that are monitored and maintained.