Machine Learning End-to-End

In this initial chapter,you will engage in the foundational stages of any machine learning project: designing an end-to-end machine learning use case, exploratory data analysis, and data preparation. By the end of the chapter, you will have a solid understanding of the early stages of a machine learning project, from conceptualizing a use case to preparing the data for further processing and model training.

This chapter will delve deep into the essential processes of model training and evaluation. It comprises four comprehensive lessons, focusing on various aspects of feature engineering, model training, logging experiments, and model evaluation.

This chapter delves into the essential elements of model deployment, a crucial phase in the machine learning lifecycle. Starting with testing, the chapter then progresses to architectural components, with a focus on feature stores and model registries. Subsequently, we will dive into the realm of packaging and containerization. The chapter concludes with an overview of Continuous Integration and Continuous Deployment (CI/CD).

In the final chapter, you will navigate the intricacies of model monitoring, a critical phase in the machine learning lifecycle.