Why Feature Stores are Important in Machine Learning

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We are living in the age of technology and have extremely busy lives. To manage our time and daily work routines effectively, we have to utilize the latest technology. In this article, we will discuss what a feature store is and why they are important in machine learning. We will also touch upon how they work to help as quickly as possible. Machines make our work easier by increasing the volumes we can produce. Now let’s talk about Machine Learning. Arthur Samuel was an American pioneer in the field of computer gaming and Artificial Intelligence. He coined the term “Machine Learning” in 1959. He wrote the first computer learning program.

What is Machine Learning (ML)?

Machine learning is the study of computer algorithms that can improve automatically through experience by the use of past data. It is an integral part of artificial intelligence. It allows computers or systems to self-learn without being explicitly programmed. It focuses on developing computer programs. There are three types of Machine Learning: supervised learning, unsupervised learning and reinforcement learning.  ML is used in a variety of applications, such as in computer vision, speech recognition, email filtering and medicines. 

What is Artificial Intelligence (AI)?

As we know, humans learn from their past experiences, and the machines follow the instructions given to them by a human. Artificial Intelligence was established in 1956 as an academic discipline. ML and AI are not the same but they are closely related. AI is a computer or a robot’s ability to perform tasks which are typically associated with intelligent beings. It means a developing system endowed with the intellectual process. AI is used in smartphones, cars, social media fields, video games, banking and in many other aspects of our daily lives.  

What is a Feature store in ML?

A Feature store is a data warehouse of features for ML. It is a tool for organizing and storing used features. When a data scientist develops some new features for an ML model, those features can be added to the feature store. This process enables those features to be available for reuse. It helps organizations for the best collaboration on ML. It is a data management layer that allows the data scientist to share and discover features. In simple terms, it is an interface between raw data and models, this is because it takes raw data then transforms it into features and is used in model training and inference. 

Why is the Feature Store Important for ML?

It is very important and powerful for machine learning. When we make an application and product catalog and have a huge number of customers, then we have to use a feature store to make things easier. Here are some key points which really show the importance of a feature store for ML:

  • It enhances the collaboration between teams because of its reusability
  • It supports health monitoring and drift detection to observe issues 
  • ML engineers and data scientists won’t need to worry anymore about calculating and values of features 
  • It is a timesaver because features are already computed for training 
  • It is a must-have when we are we are working on a project
  • It provides lineage, versioning and regulatory compliance
  • Model training and inference leads to better model accuracy

Here are some noteworthy Feature Stores. Many renowned vendors use it as a tool, such as:

  1. Amazon sagemaker
  2. Databricks
  3. Iguazio
  4. Kaskada
  5. Molecula
  6. Rasgo
  7. Vertex AI 

3 Popular Feature Store Tools


It is an open-source tool, used to manage features. Feast doesn’t stream new data, it only tracks features and regains them for inference. It simply manages data because it does not store it. It can run imminently on Google Cloud Platform (GCP). Since it is open-source, it is free to use. 


Tecton is an endeavor feature store, on top of Feast. It improves some features to make Feast more manageable for organizations. It supports the underlying features for storage and execution for pipelines for transformation. It further explores the features and includes web UI to the browser. It has a higher price tag, but organizations can adopt it easily.


Hopsworks is also enterprise-grade. It can manage the storage, transformation and serving of features smoothly and accurately. It runs on infrastructure options such as Azure, GCP, AWS, Kubernetes and on-Premise hardware. It supports a selection of data sources in which HDFS, Redshift and Snowflake are included. Like Tecton, it also includes Web UI for browsing and exploring features. It has both free and paid options..

Hopefully this article has been useful to explain all about ML, AI, FS, and the importance of Feature Stores for Machine Learning as well. If anyone finds the effort of copying and pasting feature engineering code from one project to another tedious and monotonous, the above mentioned systems could simplify your life.

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