Moving a machine learning model from a development environment to a functioning production system presents a distinct set of operational challenges. This chapter introduces Machine Learning Operations (MLOps), the discipline focused on building and maintaining ML systems efficiently and reliably.

We will establish a starting point for understanding MLOps by covering several key topics:

• A clear definition of MLOps and the problems it is designed to solve.
• The relationship between MLOps and DevOps, highlighting their important differences.
• The main goals of an MLOps strategy, from automation to reproducibility.
• The core principles that guide MLOps practices.
• Common difficulties faced when running machine learning models in production, such as data drift and model staleness.

By the end of this chapter, you will have a clear understanding of what MLOps is, why it is needed, and the fundamental ideas it is built upon.