All About Feature Stores – KDnuggets

# Introducing Feature Stores

 
Feature stores are no longer a niche infrastructure, but a key front-end that helps push the boundaries of data pipelines, particularly those involving machine learning and other AI systems. They have become a trend in the present year largely due to the industry shift from experimental machine learning model-building to the need to operationalize scalable AI-fueled solutions, products, and services.

This article gently introduces feature stores, describing their origins, main characteristics, reasons for their current significance, and popular tools at present.

# Tracing the Origins and Evolution of Feature Stores

 
The term “feature store” was coined by Uber in 2017 to simplify what they labeled as a “data pipeline jungle” and to enforce feature governance and consistency. As a result, they created a centralized repository for storing, sharing, and reusing features across multiple machine learning models and projects, at the same time that consistency between training and production data is preserved.

Not long after, in 2019, the first enterprise-level, third-party feature store vendor, Tecton, was 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 enterprise market as a whole, and the launch of their product took place in 2020. Around the same time, cloud-native feature store solutions emerged within major platforms such as Amazon Web Services (AWS), Google Cloud, and Microsoft Azure. These managed services, usually tightly integrated with their respective machine learning frameworks, have ever since continued to evolve and mature to this day.

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

Feature stores can therefore be thought of as a single source of truth for features within a (typically business-oriented) domain. Feature reuse, enforcement of consistency between model training and serving, and the foundations for governing, monitoring, and scaling machine learning operations are additional distinctive characteristics — features, if you will — of modern feature store systems.

In a feature store, features are described declaratively by specifying their business semantics, source data, transformation logic, associated metadata, and their availability for offline training and online model inference or serving.

# Understanding Feature Stores Through an Example

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

A feature store has been designed, aided by the company’s trusted cloud provider, to define and manage the relevant features shared across their fraud detection models. Such relevant features include: number of initiated user transactions in the last 24 hours, average transaction amount over the past week, number of distinct payment methods utilized by the user in the last month, and time elapsed since the user’s last transaction, among others.

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

• Business semantics: This feature describes, for a given user, the number of initiated transactions in the last 24 hours.
• Source data: The feature is derived from data in the transactions table — an event-type table containing columns for user_id, transaction_timestamps, and status.
• Transformation logic: To obtain it, a count of transactions with initiated status grouped by distinct user_id is computed, over a rolling window that spans 24 hours.
• Associated metadata:
• Owner: Fraud machine learning team.
• Type: integer.
• Window: 24h.
• Freshness 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 should be to deem it as valid for usage by the model. It is a mechanism of feature stores that helps ensure reliability and consistency in terms of machine learning models’ behavior.

Example feature specifications in a feature store | Image by Author

# Exploring the 2026 Feature Store Hype and Popular Tools

 
There are various reasons why, despite not being a brand-new paradigm, feature stores have become an important data science and AI trend at present. Here are some of them:

• With the rise of agentic AI, feature stores have seen their value multiply due to providing the high-quality, real-time data features needed by state-of-the-art AI agents to conduct complex, multi-step tasks by themselves.
• Organizations increasingly acknowledge the significance of data infrastructure rather than machine learning models built in isolation. Feature stores are the glue and foundation to help them make this shift.
• Feature stores help avoid duplicated efforts by data engineering teams, making the reuse of curated and production-ready features the new norm.
• Feature stores align with new, stricter AI regulations, regarding aspects like centralization and alignment with transparency standards.
• For domain-specific goals and KPIs, like hyper-personalization (in sectors like retail), feature stores push the boundaries of analysis in real time.
• Regarding costs, feature stores help manage escalating infrastructure costs and efficiency, preventing redundant data processing and reducing the computational overhead as a result.

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

1. Feast: An open-source store, ideal for teams with sufficient engineering resources and eager to avoid vendor lock-in.
2. Tecton (Databricks): Recently acquired by Databricks, Tecton 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 its integration with Google BigQuery and state-of-the-art generative AI models.
4. Amazon SageMaker Feature Store: Tightly integrated with AWS, it elegantly supports feature retrieval both in batch and real-time model inference.

# Concluding Remarks

 
Feature stores have gained a lot of traction nowadays in line with the latest AI advances and the rising organizational needs to keep up with continuous advances and evolving goals and needs. This article is intended to provide a gentle introduction to feature stores, outlining what they are, their characteristics, evolution, and salient tools.
 
 

Iván Palomares Carrascosa is a leader, writer, speaker, and adviser in AI, machine learning, deep learning & LLMs. He trains and guides others in harnessing AI in the real world.