This panel explores a variety of MLOps considerations to successfully implement. We look to walk through key challenges from architectural solutions, tooling issues and the frameworks that already exist in the open-source-fueled data science ecosystem that will help us in our own organisations approach to MLOps. Building a better understanding of these opportunities and their challenges helps achieve operational excellence. What's new in this area? Where will the next year of MLOps take us?