GCP AI Fundamentals - AIML Series 4 - Feature Engineering
Introduction Welcome back to the AIML Series! In this fourth installment, we'll dive deep into Vertex AI Feature Store, explore feature engineering, and cover concepts related to Apache Beam, Dataflow, and TensorFlow. With a blend of wit and information, let's make complex concepts easy to grasp and enjoyable to read. Vertex AI Feature Store Terminology and Concepts Feature Store : A centralized repository for managing and serving machine learning features. Think of it as a high-tech pantry where you keep all your ingredients (features) ready for when you need to cook (train your models). Feature : An individual measurable property or characteristic used as input to a model. For example, the age of a house in a real estate model. Entity : The primary object for which features are being stored (e.g., user, product). It’s like the main character in your data story. Feature Value : The actual data point or measurement, such as "35" for the age of a house. Feature Group :