In many machine learning projects, you will follow a series of steps: loading data, performing preprocessing, training a model, and then evaluating its performance. As these sequences become more involved, managing each step independently can be difficult and may introduce errors or make it hard to reproduce your results. This chapter introduces machine learning pipelines as a structured method to organize and automate these workflows.

You will learn to use MLJ.jl to build these pipelines, connecting data preprocessing stages with model training into a cohesive unit. We will cover methods for saving and loading your trained pipelines for later use or deployment. Additionally, you will see how to construct and adjust more complex, multi-step workflows. Finally, we will go over strategies to help ensure your machine learning experiments are reproducible, an important part of developing reliable models.