This talk will go through how we at Emplifi created and use a framework that now enables us to deploy any of our ML models as HTTP endpoints, stream consumers, or batch jobs in Spark. All of this with no (or almost no) custom coding. This in turn has also helped with automatic pipelines for retraining, deployments, and testing for us. We will show how adopting a standard for all models (MLflow) enabled us to abstract away the models' implementations and write code that works for any and all models, regardless of the underlying technology and/or dependencies. This separation of concerns also makes clear the line between where the data scientist's work ends, and where the data engineer's begins. Having one single code base for all deployments also means that updates or extensions are fast and easy to do.