Tutorial
4 min read

Feature Store comparison: 4 Feature Stores - explained and compared

In this blog post, we will simply and clearly demonstrate the difference between 4 popular feature stores: Vertex AI Feature Store, FEAST, AWS SageMaker Feature Store, and Databricks Feature Store. Their functions, capabilities and specifics will be compared on one refcart. Which feature store should you choose for your specific project needs? This comparison will make this decision much easier. But first:

Feature Store explained: What is a Feature Store?

A feature store is a data storage facility that enables you to keep features, labels, and metadata together in one place. We can use a feature store for training models and serving predictions in the production environment. Each feature is stored along with metadata information. This is extremely helpful when working on a project, as every change can be tracked from start to finish, and each feature can be quickly recovered if needed.

Before we go any further, let's look at the Feature Store data model in the diagram below.

feature-store-data-model-diagram

A Feature Store contains the set of entities of a specified entity time. Each entity type defines fields like "entity_id", "timestamp" and a list of features like "feature_1", "feature_2" and so on.

So, we can think of a Feature Store as a centralized set of entities from the whole organization:

  • Business teams provide high-level business metrics with no noise or bias from low-level data. For example, you don't want to build your fraud detection engine on data biased by the fraudulent activity of users.
  • Data scientists are interested in entities representing high-quality features to train their machine learning models. Most of the time, these features are not business metrics but rather very granular values computed from the raw data of your application (for example, how many times the user X logged in within the last hour). These high-quality features are computationally expensive to derive and hard to maintain. The last thing you want is to have every machine learning model recomputing those features at each run.

The machine learning platform needs to access those features at scale when running your models in production.

The Feature Store can solve business problems, which I mentioned in this article: MLOps 5 Machine Learning problems resulting in ineffective use of data

Still, before that, I would like to briefly introduce the solutions available on the market.

Feature Store compared

Below in the refcart, you will find a very specific comparison of the basic differences of the four most popular Feature Stores: Vertex AI Feature Store, FEAST, AWS SageMaker Feature Store, and Databricks Feature Store.

feature-store-compared

An internal feature store to manage and deploy features across different machine learning systems is key practice for MLOps. Feature stores help develop, deploy, manage, and monitor machine learning models. It allows you to improve the development lifecycle of your model and the flexibility and scalability of machine learning infrastructure. You can also use the feature store to provide a unified interface for access to features across different environments, such as training and serving.

We are in the process of completing the release of an ebook that will show you specifically step-by-step, how to build a feature store from scratch by using the Vertex AI platform, and how to resolve business problems that can occur in the Machine Learning process. We will also point out the differences between BigQuery and Snowflake, a cloud-native data warehouse. Furthermore, we will demonstrate how to use dbt to build highly scalable ELT pipelines in minutes.

If you have any questions or concerns in the area of Machine Learning and MLOps we encourage you to contact us. We have experience in the implementation and optimization of Machine Learning and MLOps processes. We have also developed original solutions in niche areas. We will be happy to serve you with our expertise.

Interested in ML and MLOps solutions? How to improve ML processes and scale project deliverability? Watch our MLOps demo and sign up for a free consultation.

Don't miss out the release of the ebook:

Power up Machine Learning process, Build feature store faster - introduce to Vertex AI, Snowflake and dbt Cloud.

The administrator of your personal data is GetInData Poland Sp. z o.o. with its registered seat in Warsaw (02-508), 39/20 Pulawska St. Your data is processed for the purpose of provision of electronic services in accordance with the Terms & Conditions. For more information on personal data processing and your rights please see Privacy Policy.

By submitting this form, you agree to our Terms & Conditions and Privacy Policy
machine learning
MLOps
Feature Store comparison
Feature Store
Vertex AI Feature Store
FEAST Feature Store
Databricks Feature Store
AWS SageMaker Feature Store
6 June 2022

Want more? Check our articles

running apache spark on aws
Use-cases/Project

Running Spark on Amazon Web Services (AWS)

When you search thought the net looking for methods of running Apache Spark on AWS infrastructure you are most likely to be redirected to the…

Read more
extracting fling flame graphobszar roboczy 1 4
Tutorial

Extracting Flink Flame Graph data for offline analysis

Introduction - what are Flame Graphs? In Developer life there is a moment when the application that we create does not work as efficiently as we would…

Read more
screenshot 2022 08 02 at 10.56.56
Tech News

2022 Big Data Trends: Retail and eCommerce become one of the hottest sectors for AI/ML

Nowadays, we can see that AI/ML is visible everywhere, including advertising, healthcare, education, finance, automotive, public transport…

Read more
getindata 6 trends big data 2021 blog
Tech News

6 Big Data Trends For 2021

2020 was a very tough year for everyone. It was a year full of emotions, constant adoption and transformation - both in our private and professional…

Read more
radiodataalessandro
Radio DaTa Podcast

Data Journey with Alessandro Romano (FREE NOW) – Dynamic pricing in a real-time app, technology stack and pragmatism in data science.

In this episode of the RadioData Podcast, Adama Kawa talks with Alessandro Romano about FREE NOW use cases: data, techniques, signals and the KPIs…

Read more
obszar roboczy 12 23blogcdci
Tutorial

Different generations of CICD tools

What is CICD? It is an acronym for Continuous Integration Continuous Delivery / Deployment. CICD can be also described as the methodology focused on…

Read more

Contact us

Interested in our solutions?
Contact us!

Together, we will select the best Big Data solutions for your organization and build a project that will have a real impact on your organization.


What did you find most impressive about GetInData?

They did a very good job in finding people that fitted in Acast both technically as well as culturally.
Type the form or send a e-mail: hello@getindata.com
The administrator of your personal data is GetInData Poland Sp. z o.o. with its registered seat in Warsaw (02-508), 39/20 Pulawska St. Your data is processed for the purpose of provision of electronic services in accordance with the Terms & Conditions. For more information on personal data processing and your rights please see Privacy Policy.

By submitting this form, you agree to our Terms & Conditions and Privacy Policy