Why You Do (& Don’t) Need An ML Feature Store

Building Machine learning (ML) models are difficult, time-consuming, and expensive.

Ask any data scientist. They spend hour upon hour collecting, scrubbing and picking the right data for an ML model that might never see the light of day!

Then, to add insult, they find themselves creating more features that could have been reused from neighbouring departments.

It’s painful, we know. But there is a solution…

ML feature stores! They’re ideal for:

  • Simplifying the development, deployment and management of ML models.
  • Storing and accessing important features across your business.
  • Automating and maintaining the data behind your features and ML models. 

So how do you know whether or not your organisation should start looking into feature stores?

Our leading ML Engineers, Dominic Kafka & Christiaan Viljoen answer that burning question for you:

Why You Need A Feature Store

The reasons to build one for your organisation are abundant. You can see for yourself:

Repeatability & Centralisation

Data often sits in various spaces around organisations. Having a feature store centralises your data and makes it easier to access.

Dominic Kafka: Feature stores allow teams to avoid spending time and effort redoing work that has already been done. Instead of data scientists having to start their modelling from scratch, using a feature store allows them to use models already developed, saving time and money.


Biassed data and algorithms can skew decision-making that result in disadvantages to low income, minority groups. With numerous cases of this, auditability is crucial.

Christiaan Viljoen: “Feature stores are more auditable. You want to make sure that the features that you use for any prediction are saved somewhere and the way that you got to those features is documented. So that you can trace it easily. Especially if there are biases in your data, e.g. not giving a certain population group credit cards.

Cost & Time Benefits

Feature stores provide a number of benefits that can save organisations both time and money.

Dominic Kafka: “Especially around computing costs. When data is dispersed across different locations or formats, it can be difficult and expensive to compute. A feature store helps to consolidate all that data into a manageable format, making things easier and more affordable.”

Why You Don’t Need a Feature Store

Feature stores aren’t necessary for every company. Besides the obvious, if there isn’t any data analysis or machine learning happening, then there is no real need for one.

And if you also only have a small amount of data that doesn’t change rapidly, then it may not yield the returns that you hope for. 

Your Infrastructure (Kinda) Sucks

If your current infrastructure won’t allow for it, or if you’re not looking to the cloud…

Kafka: “An important question that needs to be considered is where the feature store is being built. While some organisations will opt for something on-prem, a recommended choice would be to build it in the cloud if you want to do ML maturely. I don’t see a lot of opportunities to make ML work scalably in the future without cloud.” 

Viljoen: “It also depends on the position that the company is currently in… You want to know if they’ve got some form of a feature store, or have taken any actions towards building one out.”

You Don’t Quite Understand Your Data

How well do you understand your data? If you aren’t doing any transformations ( converting, processing and storing in an effective way) on it, then maybe give it a pass.

Kafka: “Does the organisation do repeatable transformations? Are they repeatedly doing similar transformations? If so, they’ve ticked the box. They’re ready to do some form of feature store. If they have that, they have a platform. Whether it’s on-prem, or in the cloud.” 

how To Get Yourself a feature store 

If these fantastic feature facilitators sounds like something that could be of benefit to your business, then we can help! 

Our ML Engineering experts will provide you with a roadmap and the skills to have a functioning feature store in no time! They’re so confident on the subject, in fact, that they’re hosting an Ask Me Anything on May 5th!

It will be a complete deep-dive into everything ML and feature related.

[So follow the link to find out more (And get your free eBook on ML Feature Stores today!)]

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