In this episode of MLOps Live, Sabine and Stephen are joined by Andy McMahon, Machine Learning Engineering Lead of the NatWest Group. They explore concepts around building your first MLOps systems and how teams can understand the processes of optimizing level 0 operations and move towards scalability. As soon as you commit a piece of code, a properly mature MLOps pipeline may be so powerful that it may be put into production immediately. However, attaining this level of maturity is extremely uncommon. Therefore, it becomes crucial to outline the requirements for creating a system that simplifies future operations, lowers failed deployments, and boosts performance. The goal is to build an MLOps system that you can easily iterate on and would not break when the time for scale and integrating components (such as model registry and feature stores) arrive. Andy demonstrates how a basic model with an optimal MLOps infrastructure will yield value more quickly than a complex model that is thrown over the fence, which may result in resource wastage. Teams can begin by redefining the deliverable expectations, simplifying them to what is truly necessary, utilizing available tools, and constantly realigning operational considerations to the business problem to be solved. Andy outlines several essential ideas, from the most basic level (MLOps at level 0), which includes no automation, to the most advanced one (MLOps level 1 and 2), which involves automating both machine learning and CI/CD pipelines.