Why are feature stores such a buzz these days? – Analytics India Magazine

“A few years ago, we noticed a pattern: teams were getting overburdened with increasing costs of maintaining their feature preparation pipelines,” says LinkedIn in a blog post while talking about its feature store, Feathr. Feature stores are actually quite a recent concept that helps to create better machine learning pipelines. A major chunk of a data scientist’s time goes into wrangling data and preparing it for analysis instead of building models. Feature stores can be of great help to solve this issue.
Uber introduced Michelangelo Palette, the first feature store, in 2017. Databricks recently announced that it has made the Databricks feature store generally available. LinkedIn also open-sourced Feathr a month ago. All major tech firms have their feature stores, like Amazon SageMaker feature store, Vertex AI feature store (Google), ML Lakes Salesforce, and Overton (Apple).
A feature store enables the discovery, documentation and reuse of features. It is a feature computation and storage service that enables features to be registered, discovered, and used for ML pipelines and by online applications for model inferencing. Feature stores stockpile feature data and offer low latency access to features for online applications. It also ensures consistent feature computations across the batch and serving APIs.
Recently, LinkedIn announced that it is open-sourcing its feature store Feathr. But most of the feature stores that exist today are still proprietary. Other popular open-source feature stores include Feast and Hopsworks. Feast was developed jointly by GO-JEK and Google Cloud for teams to store and discover features for use in machine learning projects. Hopsworks was started as a collaborative project between KTH University, RISE, and Logical Clocks. The feature store is a data management system for managing machine learning features, including the feature engineering code and the feature data. 
Priyanka Vergadi, developer advocate at Google, explains in a video the need for feature stores and the ML challenges that it can solve. She adds, “Most of the time spent by data scientists goes into wrangling data, more specifically, in feature engineering, which is transforming raw data into high-quality input signals for ML models. But this process is often inefficient and brittle.”
There are many ML feature challenges, as she points out:
Due to the sheer volume of data and algorithms big companies handle these days, feature stores are a requisite for them. Hence, we are seeing more and more feature stores coming up.
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