Stephen Foskett and Andy Thurai discuss the parallels between DevOps and MLOps with Gaetan Castelein of Tecton. We are in the middle of a shift in analytics and software engineering, with DevOps and continuous deployment, and this is colliding with the development of data analytics and big data. Machine Learning allows organizations to handle this explosion of data and build new applications and automate new business processes, but MLOps must be converged with big data and DevOps tooling to make this a reality. One key enabler of this transformation is the creation of an ML feature store, which stores curated features for machine learning pipelines. Feature stores typically enable users to build features, have standardized feature definitions, run models using these curated features, and manage MLOps.