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What Is a Feature Store in Machine Learning?

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Machine learning (ML) has become increasingly important in many industries, and feature stores play a critical role in the application of ML—including detecting financial fraud, serving relevant ecommerce product recommendations, and helping physicians to more effectively prevent and treat disease in their patients. In this article, we dive into what a feature store is and how feature stores can help data professionals better manage the complete machine learning feature lifecycle, enabling them to deploy ML pipelines in record time. 

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What Is a Feature Store?

A feature store is an emerging data system used for machine learning, serving as a centralized hub for storing, processing, and accessing commonly used features. It's making them available for reuse in the development of future machine learning models. Feature stores operationalize the input, tracking, and governance of the data as part of feature engineering for machine learning.

To fully understand why feature stores are so important, one needs to have a basic understanding of how machine learning models work. ML models use features, a measurable piece of data that can be used to teach the model to make predictions about the future based on data from the past. For example, to predict whether a customer will make a purchase within the next month, variables or features such as the sum of last month’s purchases or the number of website visits this week can be used. Similarly, for a medical-related use case, features used to describe a medical patient may include variables such as age, weight, tobacco use, exercise frequency, and current medical diagnosis. 

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Machine learning models must first undergo a training process, being fed massive quantities of historical data in the form of pre-prepared examples and features. This is what enables ML models to infer or make accurate predictions for new examples based on past experiences with similar data. Once a model has been trained to get predictions using operational data, organizations need to operationalize the pipelines that transform raw data into the same features used during training.

All data—both training and operational data—must be properly prepared for input into the model via a feature pipeline. Feature pipelines resemble data pipelines. Data output from the feature pipelines is aggregated, validated, and transformed into the appropriate format required before input into the ML model. 

How Do Feature Stores Power Machine Learning?

Feature stores function as a central repository where commonly used features are stored and processed for reuse and sharing across ML models or teams. They’re capable of not only storing and managing feature values, but they can also be used to transform raw data from a cloud data warehouse, cloud data lake, or streaming application into features useful for training of new ML models and scoring new data that feeds results to ML-powered applications. 

Benefits of a Feature Store

Feature stores have many advantages. Here’s how using them can improve your machine learning initiatives. 

Enable feature reuse

Once features have been developed, they can be saved in the feature store. This makes them available to be reused or shared between ML models and teams. Developing new features is time-intensive, keeping data scientists locked into tasks that could have been completed more efficiently by repurposing an existing feature. A well-stocked feature store can be accessed to quickly create new ML models by eliminating the need to build each new feature from scratch. 

Ensure feature consistency

Understanding how a feature was developed, how it was computed, and what information it represents is important. Maintaining consistent definitions and development documentation can be a challenge, especially for larger organizations. A centralized feature store solves this, providing a single registry for all ML features that’s easily accessible to all teams within the business.

Maintain peak model performance

When there is a discrepancy between how features are defined for training and how they are implemented in serving pipelines, it can lead to reduced performance of models in production. And because production data will evolve over time, monitoring the profile of the data set over time is important to maintain the highest model performance. To solve this problem, feature stores have centralized feature pipelines that ensure feature definitions and their implementation remain consistent across training and inference and include continuous monitoring of data pipelines.

Enhance security and data governance  

Quickly identifying what data a model was trained on and what data it was fed after deployment is important for iterating or debugging. A feature store contains detailed information for each machine learning model, such as what data was used on it and when. Feature stores that integrate into a cloud data warehouse benefit from enhanced data security that comes with this configuration, providing additional security for both the models and the data they were trained on.

Foster collaboration between teams

A feature store offers a centralized platform for the development, storage, modification, and reuse of ML features. This fosters cross-team collaboration, allowing members from multiple data science teams the ability to share ideas, and develop and track the progress of features that may be useful for multiple business applications.

Snowflake Powers Machine Learning Models and Applications

The Snowflake Feature Store (in preview) is an integrated solution for data scientists and ML engineers to create, store, manage and serve ML features for model training and inference. It consists of Python APIs accessible through the Snowpark ML library, and SQL interfaces for defining, managing and retrieving features, along with managed infrastructure for feature metadata management and continuous feature processing. By using the Snowflake Feature Store, ML teams can maintain a single and up-to-date source of truth for features used in model training and inference.  

Learn more about how to use Snowflake for AI/ML. See Snowflake’s capabilities for yourself. To give it a test drive, sign up for a free trial. 

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The Data Cloud

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Platform

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Workloads

Collaboration

Data Science & ML

Cybersecurity

Applications

Data Warehouse

Data Lake

Data Engineering

Unistore

Pricing

Pricing Options

Value Calculator

Solutions

For Industries

Advertising, Media, and Entertainment

Financial Services

Healthcare & Life Sciences

Manufacturing

Public Sector

Retail / CPG

Technology

For Departments

Marketing Analytics

Product Development

IT

Finance

Company

Overview

About Snowflake

Investor Relations

Leadership & Board

Careers

Newsroom

Speakers Bureau

ESG at Snowflake

Snowflake Ventures

Why Snowflake

Overview

Why Snowflake

Customer Stories

Partners

Services

Solutions

For Industries

Advertising, Media, and Entertainment

Financial Services

Healthcare & Life Sciences

Manufacturing

Public Sector

Retail / CPG

Technology

For Departments

Marketing Analytics

Product Development

IT

Finance

Company

Overview

About Snowflake

Investor Relations

Leadership & Board

Careers

Newsroom

Speakers Bureau

ESG at Snowflake

Snowflake Ventures

The Data Cloud

Overview

Platform

Snowflake Marketplace

Snowpark

Powered by Snowflake

Live Demo

Workloads

Collaboration

Data Science & ML

Cybersecurity

Applications

Data Warehouse

Data Lake

Data Engineering

Unistore

Pricing

Pricing Options

Value Calculator

Resources

Learn

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Developers

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