Summary
This chapter laid out in detail what are the different steps involved in creating a machine learning pipeline. This tour should be considered an initial overview of the steps involved. As the book progresses you will learn how to improve your own pipelines, but we did learn some of the best practices and most popular tools that are used to set up pipelines today. In review the steps to a successful pipeline are:
- Problem definition
- Data ingestion
- Data preparation
- Data segregation
- Candidate model selection
- Model deployment
- Performance monitoring
In the next chapter we'll delve deeper into one of the steps of the machine learning pipeline. We'll learn how to perform feature selection and we'll learn what is feature engineering. These two techniques are critically important to improve model performance.
