Chapter 1. Overview of machine learning features and Feature Store

The target audience for Feature Store is ML platform and MLOps teams with DevOps experience in deploying real-time models to production. Feature Store also helps these teams build a feature platform that improves collaboration between data engineers, software engineers, machine learning engineers, and data scientists.

In machine learning, a feature, also referred to as a field, is an individual measurable property. A feature is used as an input signal to a predictive model. For example, if a bank’s loan department is trying to predict whether an applicant should be approved for a loan, a useful feature might be whether they have filed for bankruptcy in the past or how much credit card debt they currently carry.

Features are prepared data that help machine learning models understand patterns in the world. Feature engineering is the process of selecting, manipulating, and transforming raw data into features that can be used in supervised learning. As shown in the table, a feature refers to an entire column in a dataset, for example, credit_score. A feature value refers to a single value in a feature column, such as 730.

Feature Store is an OpenShift AI component that provides an interface between models and data. It is based on the Feast open source project. Feature Store provides a framework for storing, managing, and serving features to machine learning models by using your existing infrastructure and data stores. It facilitates the retrieval of feature data from different data sources to generate and manage features by providing unified feature management capabilities.

The following figure shows where Feature Store fits in the ML workflow. In an ML workflow, features are inputs to ML models. The ML workflow starts with many types of relevant data, such as transactional data, customer references, and product data. The data comes from a variety of databases and data sources. From this data, ML engineers use Feature Store to curate features. The features are input to models and the models can then use the data from the features to make predictions.

Figure 1.1. Feature Store in the ML workflow

Feature Store is a machine learning data system that provides the following capabilities:

Feature Store is a centralized hub for storing, processing, and accessing commonly-used features that enables users in your ML organization to collaborate. When you register a feature in a Feature Store, it becomes available for immediate reuse by other models across your organization. The Feature Store registry reduces duplication of data engineering efforts and allows new ML projects to bootstrap with a library of curated, production-ready features.

Feature Store provides consistency in model training and inference, promotes collaboration and usability across multiple projects, monitors lineage and versioning of models for data drifts, leaks, and training skews, and seamlessly integrates with other MLOps tools. Feature Store remotely manages data stored in other systems, such as BigQuery, Snowflake, DynamoDB, and Redis, to make features consistently available at training / serving time.

Feature Store performs the following tasks:

ML platform teams use Feature Store to store and serve features consistently for offline training, such as batch-scoring, and online real-time model inference.

Feature Store consists of the following key components:

Feature Store provides the following software capabilities:

The Feature Store workflow involves the following tasks OpenShift cluster administrators, and machine learning (ML) engineers or data scientists:

Note: This Feature Store workflow describes a local implementation that is available in this Technology Preview release.

Cluster administrator

Installs and configures Feature Store, as described in Chapter 2. Configuring Feature Store:

ML Engineer or data scientist

You can use a web-based user interface to simplify and accelerate the creation of model development features. This visual interface helps you explore and understand your Feature Store.

You can use the Feature Store UI to access a centralized catalog of features and metadata, such as transformation logic and materialization job status. You can also view features, manage entities, and use lineage and search capabilities.

You must enable the Feature Store UI before you can use it.