Feature Engineering and Dimensionality Reduction
In this chapter, we will dive progressively deeper into the kinds of data processing steps that are common in many data science projects, and how to perform those steps using Vertex AI in Google Cloud. We’ll begin this chapter by taking a more detailed look at how features are used in machine learning workloads, and what kinds of challenges often arise concerning how features are used.
We will then transition our discussion to focus on how to address those challenges, and how to use our machine learning features effectively in Google Cloud.
This chapter covers the following topics:
- Fundamental concepts related to dimensions or features in machine learning
- An introduction to the curse of dimensionality
- Dimensionality reduction
- Feature engineering
- Vertex AI Feature Store
