Here Is Why You Need a
A Feature Store
By Christiaan Viljoen and Dominic Kafka
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Here Is Why You Need a
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Why You
Need A Feature Store
(And When To Build One)
Feature stores are an incredible, but understated resource. Especially when building machine learning (ML) models. They act as an accessible, centralised location where you’re able to store important features for ML models and their predictions. In other words, a library for your organisation’s curated data.
So what are feature stores?
Why does your organisation need one?
And when should you start building one out?
We answer some of these pressing questions in our eBook with the help of our Machine Learning experts,
Christiaan Viljoen and Dominic Kafka.
Hold On. What Is A Feature?
In feature engineering, a feature is a specific attribute of data that is useful for modelling. This can be from either a single raw data point, or an aggregation of them.
Features can be numeric or categorical, and can be extracted from data in a number of ways.
One example might include building a model to predict fraudulent transactions. A relevant feature might be whether or not a person’s spending habits seem unusual. Or if they’ve made any purchases in a different country.
Feature stores are so incredible, that they:
- Make it easier to develop and deploy models.
- Allow for better accessibility in analytics and other ML projects.
- Are an ideal option for companies looking to get the absolute best out of their ML investments.
Features are usually built from aggregations. For example, sums, averages, minimums, maximums, etc. They are then used to inform and enable a machine learning model to have the ability to predict something.
– Christiaan Viljoen.
