Knowing Vertex AI feature store – Vertex AI Feature Store

by Ron Mutschler ASP.NET Certifications, Creation of entity type, Exams of Google, Google Certification Exams, Limitations of Explainable AI

Introduction

After learning about the pipelines of the platform, we will move to the feature store of GCP. In this chapter, we will start with an understanding of the feature store, and the advantages of features followed by a hands-on feature store.

Structure

In this chapter, we will cover the following topics:

  • Knowing Vertex AI feature store
  • Hierarchy of feature store
  • Advantages of feature store
  • Disadvantages of feature store
  • Working on feature store using GUI
  • Working on feature store using python
  • Deleting resources
  • Best practices for Feature store

Objectives

By the end of this chapter, users will have a good idea about the feature store, when to use it, and how to employ it with the web console of GCP and Python.

Knowing Vertex AI feature store

Vertex AI Feature Store is a centralized repository for managing and delivering machine learning features. To speed up the process of creating and delivering high-quality ML applications, many organizations are turning to centralized feature stores to facilitate the sharing, discovery, and re-use of ML features at scale.

The storage and processing power, as well as other components of the backend infrastructure, are handled by Vertex AI Feature Store, making it a fully managed solution. As a result of this strategy, data scientists may ignore the difficulties associated with delivering features into production and instead concentrate on the feature computation logic.

The feature store in Vertex AI is an integral aspect of the overall system. Use Vertex AI Feature Store on its own or include it in your existing Vertex AI workflows. For instance, the Vertex AI Feature Store may be queried for information to be used in the training of custom or AutoML models.

Hierarchy of feature store

The collection of entities for a certain entity time is stored in a feature store. Fields like entity ID, timestamp, and a series of attributes like feature 1, feature 2, and so on, are all defined for each entity type. The hierarchy of the feature store is described in Figure 9.1:

Figure 9.1: Hierarchy of feature store

  • Feature store: A top-level container for entity types, features, and their values.
  • Entity type: A collection of semantically related features (real or virtual).
  • Entity: An instance of the entity type.
  • Feature: A measurable property or attribute of an entity type.
  • Feature values: These contain values of the features at a specific point in time.