You've trained a machine learning model, perhaps a classifier or a regressor. It performs well on your test data. What happens next? This chapter addresses that question by introducing the concept of model deployment. It's the process of taking a trained model and making it available to provide predictions on new, unseen data in a live environment.

In this chapter, you will learn:

• What machine learning deployment means and why it's a necessary step after model development.
• How deployment fits into the broader machine learning workflow.
• An overview of different approaches used to deploy models.
• Some common difficulties faced when putting models into production.

By the end of this chapter, you'll have a foundational understanding of model deployment's purpose and place in the machine learning lifecycle.