Feature stores are rapidly growing popular as organizations look to improve their machine learning productivity and operations (MLOps). With the advancements in MLOps, feature stores are becoming an essential component of the machine learning infrastructure, helping organizations to improve the performance and explainability of their models, and accelerate the productionization of new models. These stores serve as centralized repositories providing a single source of truth for features, enabling teams to collaborate and reuse features across multiple projects, and streamlining their machine learning pipeline.
In this article, we will highlight the basics of a feature store and discuss the various ways your organization can benefit from it.
Before getting into the details of a feature store, it is important to highlight what features are. Features are the independent variables used as input to train ML models, including demographic data, sensor readings, or text data. They are created in relevance to the machine learning problem at hand through a process called feature engineering.
A feature store is a centralized repository designed to store and manage features used in machine learning models. The features in a feature store are readily available to create complex machine-learning pipelines for model operationalization. Some of these stores also include functionality for versioning, lineage tracking, and feature selection and engineering, making it easier for different teams to access and reuse features to understand and improve the performance of their models over time.
In addition to feature storage, a feature store provides many operational benefits to organizations for improving their model performance. Some of these benefits include the following:
The feature production stage is one of the most time-consuming ML model development lifecycle processes. It requires feature extraction, domain expertise, accurate calculations, heavy computations, and validations for thousands of features at a time.
Feature stores tackle this problem by streamlining and automating many feature engineering processes, resulting in faster time-to-production and reduced errors. They also reduce the time it takes to move and prepare data for training by caching features for future reuse.
Moreover, large organizations with many teams can accelerate their feature serving by providing a centralized feature repository, saving huge chunks of model training and development time.
An organization working with feature stores can generalize their machine learning project workflows that all teams can quickly adopt for all ML algorithms. A centralized feature development, storage, modification, and reuse, allows different teams to access and use the same features for distinct ML models. As a result, feature stores bridge the gap between data science and data engineering teams enabling data engineers to build and maintain feature stores while data scientists access and use features to develop models, improving collaboration and communication.
Overall, feature stores foster cross-team collaboration, allowing members from multiple teams to share ideas, and develop and track the progress of features that may be useful for various business applications.
Maintaining peak model performance relies heavily on feature consistency and continuous data monitoring. This is where feature stores shine with their ability to maintain consistent feature definition and implementation from training to production. By making it easy for teams to access and reuse features, feature stores ensure that models are built using the best available features by experimenting with different features and comparing the results. This can help them identify new features that can improve model performance and reduce the time and effort required to develop new models.
Feature stores also include continuous data pipeline monitoring capabilities that allow teams to track the performance of features over time. This can make it easier to identify when a feature's performance has degraded and take action to address the issue.
Another benefit of feature stores is their capability to improve data governance by ensuring feature governance. By standardizing the feature definitions and naming conventions, feature stores make it easier for organizations to enforce policies around feature usage, access, and quality. In this way, every team member accessing a feature knows exactly how it is computed and what information it represents.
The centralization of features significantly contributes to enhancing control over feature storage, management, and tracking. Moreover, feature store functionalities like versioning, rollback, and approval workflows further aid the effective maintenance of data integrity and governance.
It can be very challenging for organizations to maintain feature consistency across their definition, naming, development, computation, documentation, and management. Thanks to feature stores, they now have a one-stop solution for consistently managing these stages in a unified registry for all features that’s easily accessible and understandable to all teams across different models.
One way feature stores ensure consistency is through versioning, which allows multiple feature versions to be stored and tracked. It enables teams to work on different versions of a feature simultaneously while also letting them roll back to a previous version if required.
Additionally, feature stores ensure the proper testing, documentation, and evaluation before passing them on to production. They also provide automated feature validation, including checks for data completeness, data types, format, and range of values, resulting in consistent features that data teams can use to produce robust ML models.
A common problem with productionizing models is the 'online/offline skew' problem, referring to the differences in model performance during model training and inference. The difference arises from using different technologies and languages to generate online and offline features.
A feature store is an environment-agnostic solution to this problem that uses the same data to provide consistent features to training and inference settings. They use version control and automated validation of features in both environments to ensure consistency.
By improving feature accessibility and reusability, feature stores allow teams to use the exact same features in their model training and production. As a result, feature stores enhance the consistency of model performance between training and inference and prevent skewed model predictions.
Today, feature stores are a part of various machine learning projects, providing a systematic approach to organizations for scaling their projects. These feature stores are generally a part of larger ML infrastructure platforms. An example of such a platform is Qwak – a fully-managed machine learning platform that unifies machine learning engineering and data operations.
Qwak's feature store is the best-in-class feature repository, designed for the entire feature lifecycle, from development to deployment. The Qwak team fully manages all aspects of the feature store, so your teams can focus on building and delivering innovative features. The feature store also easily integrates with any data source, regardless of its type.
Some of the key capabilities of Qwak's feature store include the following:
Qwak simplifies the productionization of machine learning models at scale. Qwak’s Feature Store and ML Platform empower data science and ML engineering teams to Build, Train and Deploy ML models to production continuously.
By abstracting the complexities of model deployment, integration, and optimization, Qwak brings agility and high velocity to all ML initiatives designed to transform business, innovate, and create a competitive advantage.
Are you looking for a sophisticated feature store for the frictionless productionization of your ML models? Check out Qwak to learn how you can easily transform your data into features. Get started today for free.