After selecting, building, and tuning models, the next step is model deployment. The goal of model deployment is to produce outputs that lead to a decision or action.

In a common scenario, model predictions and other variables are inputs to an optimization problem. The solution to that problem produces raw outputs that must be translated and communicated to business experts and decision makers. If the recommendations make sense from their perspective, they can decide to put them into play.

Here’s some examples of what those decisions might look like after evaluating and translating model outputs:

• Raise price
• Launch the promotion
• Change the policy
• Change the mixture

In a data science application, model deployment is often automated while still allowing analyst users to override and influence the model’s recommendations.