Machine Learning in Production: Lessons Learned from Deploying Our First ML Model

Machine learning models typically come in two flavors: those used for batch predictions and those used to make real-time predictions in a production application. These are known as offline and online models, respectively. Offline models, which require little engineering overhead, are helpful in visualizing, planning, and forecasting toward business decisions. On the other hand, online models require substantial engineering effort and are used to personalize a customer's experience via recommendations. Understanding which model to use based on project needs is critical because it not only dictates the deployment process, but also influences how the model is trained.