All About Feature Stores – KDnuggets

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# Introduction to Feature Stores

Feature stores No longer a dedicated infrastructure, but a critical front-end that helps push the boundaries of data pipelines, especially those involving machine learning and other AI systems. These have become a trend in the current year largely due to the industry’s need for experimental machine learning model building to run scalable AI-fueled solutions, products and services.

This article briefly introduces feature stores, describing their origins, key features, reasons for their current importance, and currently popular tools.

# Tracing the origins and evolution of feature stores

The term “feature store” was coined by Uber In 2017 to simplify what they labeled the “data pipeline jungle” and implement feature governance and consistency. As a result, they created a central repository to store, share, and reuse features across multiple machine learning models and projects, while preserving consistency between training and production data.

Shortly thereafter, in 2019, the first enterprise-level, third-party feature store vendor, Tectonwas founded by the same former Uber engineers who contributed to Uber’s internal feature store. Their goal was to bring commercial feature store solutions to the overall enterprise market, and their product launch took place in 2020. At the same time, cloud-native feature store solutions emerged in major platforms such as Amazon Web Services (AWS), Google Cloudand Microsoft Azure. These managed services, usually tightly integrated with their respective machine learning frameworks, have continued to evolve and mature ever since.

But what is a feature store? It can be defined as a central platform or system where all data features are defined and managed, not associated with a single, specific dataset, but with the entire machine learning domain—a set of models under the same overarching business goals—or organization. In a feature store, features are declaratively defined by defining their business vocabulary, source data, transformation logic, associated metadata, and their availability for offline training and online model estimation or serving.

Hence feature stores can be thought of. The only source of truth (usually business-oriented) for features within a domain. Additional features include feature reuse, consistency enforcement between model training and serving, and foundations for governing, monitoring, and scaling machine learning operations. Featuresif you will — of modern feature store systems.

In a feature store, features are declaratively defined by defining their business vocabulary, source data, transformation logic, associated metadata, and their availability for offline training and online model estimation or serving.

# Understanding feature stores through an example

To better understand the key concepts and functions related to feature stores, let’s consider the example scenario of an e-commerce company that is building a set of machine learning models for fraud detection.

A feature store is designed, with the help of the company’s trusted cloud provider, to define and manage the relevant features shared by their fraud detection models. Such relevant characteristics include: the number of user transactions initiated in the last 24 hours, the average transaction amount during the last week, the number of different payment methods used by the user in the last month, and the time elapsed since the user’s last transaction, among others.

Now, let’s take a closer look at one of these features to better understand what the Feature Store “has to say” about it. Consider an example feature. user_transaction_count_24h:

• Business terms: This feature tells, for a user, the number of transactions initiated in the last 24 hours.
• Source Data: The feature is derived from the data. transactions table — An event type table containing columns. user_id, transaction_timestampsand status.
• Change logic: To achieve this, count transactions with initiated Separate classification user_id Calculated over a rolling window spanning 24 hours.
• Associated metadata:
• Owner: Fraud Machine Learning Team
• Type: integer.
• window: 24h.
• Refresh SLA (Service Level Agreement): 5 minutes.
• Availability: Available for both offline training and online serving.

Importantly, the freshness SLA refers to how recent a feature value must be for the model to use it. It is a mechanism of feature stores that helps ensure behavioral reliability and consistency of machine learning models.

An example of a feature specification in the feature store Photo by author

# 2026 Feature Store Hype and Finding Popular Tools

There are various reasons why, while not an entirely new paradigm, feature stores have become an important data science and AI trend right now. Some of them are:

• With the rise of agentic AI, feature stores have seen their value multiply due to the high-quality, real-time data features required by sophisticated AI agents to perform complex, multi-step tasks on their own.
• Organizations increasingly recognize the importance of data infrastructure rather than machine learning models built in isolation. Feature stores are the glue and foundation to help with this transformation.
• Feature stores help data engineering teams avoid duplicated efforts, making reuse of curated and production-ready features the new norm.
• Feature stores are compatible with new, stricter AI regulations, regarding aspects such as alignment with centralization and transparency standards.
• For domain-specific goals and KPIs, such as hyper-personalization (in sectors like retail), feature stores push the limits of real-time analysis.
• With respect to costs, feature stores help manage growing infrastructure costs and performance, prevent redundant data processing and consequently reduce computational overhead.

Some of the popular feature store tools used by a large number of companies to leverage advanced AI applications are:

1. invitation: An open source store, ideal for teams with substantial engineering resources and eager to avoid vendor lock-in.
2. Tecton (Data Bricks): Recently acquired by Databricks, Tekton is a fully managed, scalable solution for enterprises, ideal for managing complex real-time data pipelines.
3. Google Cloud Vertex AI Feature Store: It stands out for integration with it. Google BigQuery and state-of-the-art generative AI models.
4. Amazon Sage Maker Feature Store: Tightly integrated with AWS, it elegantly supports feature retrieval in both batch and real-time model inference.

# Concluding Remarks

Feature stores today have gained a lot of attention due to the latest advancements in AI and growing organizational needs to keep up with the continuous advancements and evolving goals and needs. The purpose of this article is to provide a gentle introduction to feature stores, outlining what they are, their features, evolution, and featured tools.

Iván Palomares Carrascosa He is a leader, author, speaker, and consultant in AI, Machine Learning, Deep Learning and LLMs. He trains and guides others in using AI in the real world.