This module introduces Vertex AI Feature Store.

Feature engineering is often the longest and most difficult phase of building your ML project. In the feature engineering process, you start with your raw data and use your own domain knowledge to create features that will make your machine learning algorithms work. In this module we explore what makes a good feature and how to represent them in your ML model.

This module reviews the differences between machine learning and statistics, and how to perform feature engineering in both BigQuery ML and Keras. We'll also cover some advanced feature engineering practices.

In this module you will learn more about Dataflow, which is a complementary technology to Apache Beam and both of them can help you build and run preprocessing and feature engineering.

In traditional machine learning, feature crosses don’t play much of a role, but in modern day ML methods, feature crosses are an invaluable part of your toolkit. In this module, you will learn how to recognize the kinds of problems where feature crosses are a powerful way to help machines learn.

TensorFlow Transform (tf.Transform) is a library for preprocessing data with TensorFlow. tf.Transform is useful for preprocessing that requires a full pass the data, such as: - normalizing an input value by mean and stdev - integerizing a vocabulary by looking at all input examples for values - bucketizing inputs based on the observed data distribution In this module we will explore use cases for tf.Transform.

This module is a summary of the Feature Engineering course.