Adopting the Best Feature Store: A Brief Comparison of Platform Providers
What is a Feature Store?
Feature store is now one of the most critical elements of MLOps. It helps the data science communities to store and run statistical analysis on a large number of data coming from different sources such as streaming data, real-time data, batch data, etc.
Being integrated into the MLOps pipeline, it provides convenience to data scientists to automate their task of data pre-processing and feature engineering so that they can be fed into the model for training.
Many tech giants are now providing their feature stores to reduce the time of data munging, pre-processing, feature engineering, statistical analysis, etc. It is important for a data scientist to know what platform they should use to provide an optimized solution.
In this blog we’ll be looking at different types of feature stores provided by different organizations and comparing them based on their information provided .
Below is the list of some feature stores being used within the data science community.
All the above mentioned services provide the same kind of feature store services, but they can vary differently in terms of their performance, storage, integration with third-party services, and so on.
Instead of watching hourly long tutorials or understanding about every feature store can be hectic, one good approach to understand about these feature stores is making comparisons between them.
The comparisons are divided into 2 categories: commercial information and feature store capabilities. We will be discussing comparisons on both categories one after another.
A commercial information of any service focuses on its non-technical aspects such as name, type, duration, history, pricing, packages, etc.
Below is a comparison table of commercial information between different feature stores.
Feature Store Capabilities
This section of comparison focuses on the technical capabilities of a feature store.
A feature store consists of many technicalities such as storing ample amount of data, meta-data of training models, statistical information of the dataset, etc.
Every company that provides Feature Store as a service have their own capabilities that are different in their own ways. Below is a comparison table (series of images)of the feature stores from different platforms.
Choosing the best feature store for your work
Choosing a feature store for a project is critical. By dividing the feature stores into two categories mentioned above can help you pick the best one according to your requirements and resources.
Frist, it is necessary to perform an assessment on the feature’s commercial characteristics. This assessment can help the client to validate that either any feature store is meeting their needs or not.
For example, if a newly established startup wishes to buy a feature store, therefore, in-terms of commercial requirement, they may look upon its pricing model. Going over budget may cause burnout in funds.
After that, they can check either it is available only on cloud or on-premises. For example, if the business doesn’t heavily rely on cloud so they can go for on-premise and it can also help to lower total cost .
After commercial requirements, it is now important to look upon the capabilities of the feature store. It is necessary to make sure that any store you choose should meet your operational workflows requirements.
For example, is the feature store providing data quality monitoring and drift detection services in one because for a data scientist, it is important to detect concept drifts with data quality as well. Or is the feature store providing APIs for online data store or not?
In the area of MLOps, the community is growing and evolving at high pace. And data is also changing with a very high speed which, therefore, adds complexity that requires intensive amount of processing, monitoring, and storage.
Due to this, a feature store becomes a need. With the help of the above information, any organization will have the power to pick the best feature store according to their specific requirements and resources.
It will eventually help them to simplify the complexities and automate the pipelines for a longer period.
1. Feature Store for ML evaluation and comparison. (2021, November 25). MLOps Community. https://mlops.community/learn/feature-store/
2. Kemper, F. (2021, August 11). On-Premise vs Cloud: Advantages and Disadvantages. On-Premise vs Cloud: Advantages and Disadvantages. https://www.empowersuite.com/en/blog/on-premise-vs-cloud#:%7E:text=Probably%20the%20biggest%20advantage%20of,it%20comes%20to%20compliance%20issues.
Hassan Sherwani is the Head of Data Analytics and Data Science working at Royal Cyber. He holds a PhD in IT and Data Analytics and has acquired a decade worth experience in the IT industry, startups and Academia. Hassan is also obtaining hands-on experience in Machine (Deep) learning for energy, retail, banking, law, telecom, and automotive sectors as part of his professional development endeavors.