Feast feature store basics in MLOps - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is a feature store in machine learning?

A feature store is a system that stores and manages features used for machine learning models. It helps teams reuse, share, and serve features consistently during training and production.

beginner

What is Feast in the context of feature stores?

Feast is an open-source feature store that helps manage, store, and serve machine learning features at scale, making it easier to build and deploy ML models.

intermediate

What are the two main parts of Feast's architecture?

Feast has two main parts: the offline store, which stores historical feature data for training, and the online store, which serves features in real-time for model predictions.

intermediate

How does Feast ensure feature consistency between training and serving?

Feast uses the same feature definitions and data sources for both training and serving, ensuring that models get consistent feature values in both phases.

beginner

What is an entity in Feast?

An entity is a unique identifier for the object you want to describe with features, like a user ID or product ID. Entities link features to real-world objects.

What is the primary purpose of Feast?

A To train machine learning models

B To store raw data for analytics

C To manage and serve machine learning features

D To visualize model performance

Feast is designed to manage and serve features for ML models, not to train models or store raw data.

Which part of Feast stores historical feature data for training?

A Online store

B Offline store

C Feature registry

D Model registry

The offline store holds historical feature data used during model training.

What does an entity represent in Feast?

A A data pipeline

B A machine learning model

C A feature value

D A unique object like a user or product

Entities uniquely identify real-world objects that features describe.

How does Feast help maintain feature consistency?

A By using the same feature definitions and data sources for both training and serving

B By retraining models frequently

C By using different data sources for training and serving

D By storing features only in the online store

Feast ensures consistency by using the same feature definitions and data sources in both training and serving.

Which of these is NOT a component of Feast?

A Model training engine

B Offline store

C Feature registry

D Online store

Feast does not include a model training engine; it focuses on feature management.

Explain what a feature store is and why it is useful in machine learning projects.

Think about how teams share and use features for models.

Describe the main components of Feast and their roles.

Focus on how Feast organizes and serves features.