Feature stores organize the data processing that drives machine learning models. To support model training and production inference, ML models have specific data access needs. The Feature Store acts as a bridge between your raw data and the model's interfaces. Feature Stores enable data scientists to automate the processing of feature values, produce training datasets, and offer features online with production-grade service levels, thereby creating this abstraction.
What is the purpose of a Feature Store?
Feature Stores address these issues by allowing data teams to:
- Collaboratively create a feature library utilizing standard feature definitions.
- With just a few lines of code, you can generate accurate training datasets.
- Deploy features to production in real-time by following DevOps engineering best practices.
- Feature sharing, discovery, and re-use throughout an organization
Feature Store Components
Because the Feature Store is a new concept, the precise definition is continually changing. The following are frequent features of a feature store:
- Version-controlled code is defined as features in the Feature Registry. The feature registry is a centralized catalogue of all feature definitions and metadata. It enables data scientists to find, search for, and collaborate on new features.
- Feature stores organize data pipelines to transform raw data into feature values. They can consume batch, streaming, and real-time data to blend historical context with the most up-to-date information.
- Feature stores offer both online storage for low-latency retrieval at scale and offline storage for cost-effectively curating historical datasets, which are kept in the Feature storage.
- Feature stores provide an API endpoint for serving low-latency online feature values.
- Feature stores monitor data quality as well as operational indicators. They can check data for accuracy and detect data drift. They also keep an eye on key indicators related to feature storage (capacity, staleness), as well as feature serving (latency, throughput).
What to consider when choosing a Feature Store
Users can now choose from a wide range of feature store products. AWS, Databricks, Google Cloud, Tecton, and Feast (open source) are just a few examples. Not all feature stores, however, are considered equivalent. When selecting an offering, a user should consider the following factors:
- Integrations and the ecosystem: Some feature stores are strongly connected with a unique environment. The AWS SageMaker feature store, for example, is designed to function nicely with the SageMaker ecosystem. Other feature stores, such as Feast or Hopsworks, are not connected to a certain ecosystem and work across clouds. Are you committed to a certain environment or seeking a more adaptable solution?
- Data infrastructure: The majority of feature stores are built to orchestrate data flows over existing infrastructure. For example, the Databricks feature store is intended to run on Delta Lake. Some feature stores come with their data architecture, such as object storage and key-value stores. Do you want to repurpose existing data infrastructure or build new data infrastructure from the ground up?
- Delivery model: Some feature stores are available as fully managed services. Other feature stores require self-deployment and management. Do you prefer the fully managed services or the freedom of self-managed solutions?
- Scope of feature management: The majority of feature stores are concerned with resolving the serving issue. They offer a standard method for storing and serving feature values, but those feature values must be handled outside the feature store. Other feature stores, such as Databricks, manage the entire feature lifetime, including feature transformations and automated pipelines. The latter is very handy for doing sophisticated changes such as streaming or real-time features.
