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What Is A Feature Store in Machine Learning? Your Guide to Feature Stores in 2024.

Companies that operate with big volumes of data are frequently plagued with ineffective and costly feature engineering processes. This prevents them from putting together a complex machine learning process. Fetching data for Machine Learning purposes takes a long time, however it’s unclear whether there are any conflicts between ingestion and model service. Project stakeholders may lose faith in favorable Machine Learning outcomes as a result of the slower process and inability to repeat findings. What can we do to avoid this? Part of the solution is the Feature Store. This is an important component of the data science infrastructure since it ensures a consistent pipeline for end users.

A Feature Store is a service that consumes vast amounts of data, computes features, and stores the results. Machine learning pipelines and internet applications can easily access data with a Feature Store. Feature Stores which are implemented as a dual database are meant to provide data both in real-time and in batches: Low-latency data is served via online feature stores to online applications. MySQL Cluster, Redis, and Cassandra DB are just a few examples. Offline feature stores are scale-out SQL databases that supply data for AI model development while also allowing for feature governance for explanation ability and transparency.

Hive, BigQuery, and Parquet are some examples.

What is a Feature Store and its benefits?

So what is a Feature Store? A feature store is a tool that allows you to save frequently used features. Data scientists can contribute features to a feature store when they create features for a machine learning model. As a result, such functionalities can be reused. When new instances (e.g., app users,, business customers, or product catalog items) are introduced, the previously generated characteristics are pre-computed so that they may be inferred. Throughout the lifespan of an ML project, feature stores serve as a central hub for feature data and information.

A feature store’s data is used for feature engineering and exploration, iteration, training, and debugging of models, discovery, and sharing of features, functioning as a model for inference creation and monitoring the health of the operation. Feature stores enable ML firms to achieve economies of scale by allowing cooperation. When you register a feature in a feature store, it becomes immediately available for use by other models across the business. This saves data engineering duplication and allows new machine learning projects to start with a library of vetted production-ready features.

Three specific advantages of feature stores make them invaluable, among other things: they allow for easy feature reuse across the company; they make it simple to standardise feature definitions and naming conventions; they allow businesses to achieve consistency between models developed offline and models deployed online. Effective feature stores are modular systems that may be tailored to the area in which they’re used. A feature store is generally made up of five key components. We’ll go through some important feature store tools in the rest of this piece.

Important Feature Store Tools

The feast is a feature store that is open-source and can be used to manage features. The feast does not calculate features or stream fresh data; instead, it tracks and returns features for training and inference. It doesn’t store data, instead, it handles data from external sources such as Google BigQuery,

Google Cloud Storage (GCS), and Amazon S3. It can operate on Google Cloud Platform (GCP) or Amazon Web Services (AWS) and Kubernetes. AWS support will be improved in future editions. It is free to use because it is open-source, but it may have a steeper learning curve to interact with linked storage and transformation technologies.

Tecton is a feature tool for businesses developed on top of Feast. It adds capabilities to make Feast more manageable for businesses, such as storing underlying features and executing transformation processes. It also has a web interface for browsing and exploring features. Tecton is more expensive than Feast because it is a managed solution, but it will be easier for enterprises to acquire and use.

Hopsworks is a business-grade feature store that can handle feature conversions, storage, and retrieval/serving. It can run on a broad range of infrastructure, including AWS, Azure, Google Cloud Platform, Kubernetes, and even on-premise hardware. It also works with a wide range of data sources, such as Snowflake, Redshift, and HDFS. Feature stores are a great tool for businesses that want to create a large number of models based on one or a few entities (e.g. users, customers, products). A feature store’s main advantage is that it contains the logic of feature transformations, allowing it to automatically transform fresh data and provide examples for training and inference.

A feature store might substantially improve your life if you find yourself repeatedly coding up feature conversions or copying and pasting feature-engineering code from project to project.