ML is increasingly making its way into production to power customer-facing applications and business processes. This transition from batch to operational ML raises new organizational challenges. Data scientists and engineers now have to work collaboratively as a single team. This requires adaptation on both sides – combining data science and engineering processes into a well-integrated MLOps machine. Our panel of data scientists will provide their perspective on how data engineers can support this transition and more effectively work with data science teams.