Chapter 4: Machine Learning Pipelines
In this chapter, we will explore and implement machine learning (ML) pipelines by going through hands-on examples using the MLOps approach. We will learn more by solving the business problem that we've been working on in Chapter 3, Code Meets Data. This theoretical and practical approach to learning will ensure that you will have comprehensive knowledge of architecting and implementing ML pipelines for your problems or your company's problems. A ML pipeline has modular scripts or code that perform all the traditional steps in ML, such as data preprocessing, feature engineering, and feature scaling before training or retraining any model.
We begin this chapter by ingesting the preprocessed data we worked on in the last chapter by performing feature engineering and scaling it to get it in shape for the ML training. We will discover the principles of ML pipelines and implement them on the business problem. Going ahead, we'll look...
