Feature Store Overview

Qwak's Feature Store is a data platform for machine learning models that facilitates the discoverability, reuse and accuracy of features.

It provides a centralized method for developing features using batch data, streaming data, or on-the-fly computation, and for serving those features instantly or retrieving them as training data. It also allows the discovery and reuse of available features, instead of recreating identical or similar ones.

The Feature Store serves the following main purposes:

Source of truth: Creating a single and discoverable source of truth for features that are to be used by machine learning models.
Feature collaboration: Allowing data scientists and machine learning engineers to share features between projects.
Training/serving skew: Systematically ensuring the matching between features that have been generated online and offline

Features Definition

The Qwak Feature Store has three main concepts:

Entities: Features created via the Feature Store are tied to a specific business entity. For example: registered users, transactions, or merchants.
Data Sources: External data sources that are the features' ingestion source.
Feature Sets: The operational unit of the Feature Store. A computation definition which takes raw data as input and output features. Types of feature sets include:
- Batch: Defining features based on batch data sources (e.g. Snowflake, BigQuery etc.).
- Streaming: Defining features based on streaming data sources (e.g. Kafka).
- Real-time: Features based on data coming on the inference request.

Feature Consumption

With the Qwak Feature Store you can define features once, calculate them once, and reuse them any time.

Qwak's Feature Store systematically ensures there are no discrepancies between data generated for training and serving, since both the offline and the online stores are populated from the same singular feature extraction process.

Inference: Serve an entity's up-to-date feature values from one centralized location.
Training: Keep a log of all features, and then retrieve them for training, at any point in time.