The daily job of a Data Scientist ranges from a variety of tasks: improving models performance or dealing with framework structure implementations. Machine learning as a service, a hot topic in the field, implies thinking about architecture to allow constant improvements in performance for our products. This presentation shows one architecture design using RESTful resources, document oriented databases and pre-trained pipelines to achieve real-time predictions of time series with high availability, scalability and freedom to Data Scientists work directly on improving the accuracy rate of our products. We fine tunned to work on time series forecasting which is a very challenging field that still needs better solutions in terms of innovative modeling. During the presentation will be shown how these decisions keep our Data Scientists focused on working with real data and thinking about improvements that can reach a large volume of time series instead of singular and localized actions.