As machine learning engineers or data scientists, we all got to the point where we built our beautiful models with wonderful test results to end up using them just in a PowerPoint presentation. The most standard way of interacting with a model is to use it in an offline setup, where we have some kind of dataset to play with. This is ok for experimentation and to build your initial model. But, the next step would be to put our precious model out in the wild so people can use it. This is what model serving is all about. It represents the mechanism of deploying the model so other people can interact with it.