A data science feature that everyone needs to be aware of.

Shutterstock
Shutterstock
Source: Shutterstock

What is a feature store?

If data is the new gold (overused, but true nonetheless) I would say that features are actually the Gold bullion and therefore need to be treated accordingly. In order to get to the gold, you need to do some digging and hard work, which is also true for finding the right features.

The process of creating features is called feature engineering which is a pretty complicated yet critical component for any machine learning process. Better features mean better models resulting in a better business outcome.

Generating a new feature takes a tremendous amount of work — and creating the pipeline for building the feature is just one aspect. In order to arrive at that stage you probably had a long process of trial and error, with a large variety of features, until you got to a point where you were happy with your singular new feature. Next, you needed to calculate and store it as part of an operational pipeline, which then differs, depending if the feature is either online or offline. …


A data science feature that everyone needs to be aware of

Shutterstock
Shutterstock
Source: Shutterstock

What is a feature store?

If data is the new gold (overused, but true nonetheless) I would say that features are actually the Gold bullion and therefore need to be treated accordingly. In order to get to the gold, you need to do some digging and hard work, which is also true for finding the right features.

The process of creating features is called feature engineering which is a pretty complicated yet critical component for any machine learning process. Better features mean better models resulting in a better business outcome.

Generating a new feature takes a tremendous amount of work — and creating the pipeline for building the feature is just one aspect. In order to arrive at that stage you probably had a long process of trial and error, with a large variety of features, until you got to a point where you were happy with your singular new feature. Next, you needed to calculate and store it as part of an operational pipeline, which then differs, depending if the feature is either online or offline. …


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Almost every customer I meet is in a certain stage of developing an ML-based application. Some are just at the beginning of their journey while others are heavily invested. It’s fascinating to see how data science, a once commonly used buzz word, is becoming a real strategy for almost any company.

In the following post, I’ll address one of challenges that customers bring up time and again — running and tuning experiment tracking. …

About

Adi Hirschtein

Hands-on product, passionate about building ML/AI products. VP product @iguazio https://www.linkedin.com/in/adihirschtein/

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