Chapter 17 Model deployment (MLOps)

Building and training a model is only the first step in the lifecycle of a machine learning application. The next step is to deploy the model into production. This is the process of integrating the model into an existing production environment, so that it can be used to make predictions on new data.

Figure 17.1: Modeling workflow

There are many different ways of deploying a model and it very much depends on the infrastructure of your organization.

The vetiver package provides basic functionality to

version and publish models
deploy models into production
monitor models in production

The documentation for vetiver takes you through the steps of deploying a model into production. You can find the documentation at https://vetiver.rstudio.com/.

To learn more about deploying models, look for resources on MLOps.