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.
What Is a Feature Store?
A feature store is an emerging, ML-specific data system used to centralize storage, processing, and access to frequently used features, 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.
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 Plus Tecton and Feast Power Machine Learning Applications
Tecton is an industry-leading enterprise feature platform that seamlessly integrates with Snowflake. This pairing creates opportunities to centralize feature logic and simplify feature management, allowing users to define and manage features as code using a declarative framework. Features created in Tecton can be version controlled, unit and integration tested, and deployed via CI/CD processes. All features are stored and processed directly in Snowflake, ensuring proper data governance and security.
Pairing Snowflake’s powerful processing engine with Tecton’s Feature Platform helps organizations securely and reliably store, process, and manage the complete lifecycle of machine learning features for production. Whether the feature pipelines are feeding a model scoring data in batch or in real time, Tecton’s integration with Snowpark for Python ensures the same transformation applied to data in real time also takes place when transforming data in batch.
Snowflake is also now integrated with Feast, the popular open-source feature store. This integration streamlines how data teams can store, process, and manage ML features in Snowflake. Feast also helps teams keep Snowflake and low-latency storage in sync when running real-time inference for use cases such as fraud detection or recommendation engines.