Building a machine learning model is more than just running a training script. It is an end-to-end process that turns raw data into a functional, monitored application. This entire process is called the machine learning lifecycle, and it forms the operational backbone for any MLOps practice. Without a clear understanding of these stages, automation and reproducibility become difficult to achieve.

In this chapter, we will map out this lifecycle from start to finish. We begin with the initial steps of data ingestion and preparation. From there, you will learn how models are trained, evaluated, and validated to ensure they perform as expected. We will then examine different strategies for deploying a model into a production environment. Finally, you will see how monitoring a deployed model and creating a feedback loop enables continuous improvement, ensuring the model remains effective over time.