Model Serving Infrastructure

In the machine learning (ML) community, there is a common saying that the ROI of an ML project starts when the model is in production. This phrase reminds us that the true value of a ML project is realized when the trained model is deployed and actively used in production. The model serving infrastructure plays a crucial role in operationalizing ML models in production and integrating the ML projects into the operations of an organization, such as predicting customer churn, detecting fraudulent activities, personalizing customer experience, improving the quality of products and services, and more.