LinkedIn

LinkedIn today announced that its open source feature store, dubbed Feathr, is joining LF AI & Data, the Linux Foundation's umbrella foundation for.

Microsoft was also involved with the open-sourcing of Feathr back in April. According to Lin, LinkedIn engineers have worked with their Microsoft Azure colleagues to ensure Feathr runs well on Azure and is integrated with other Azure products and projects. Since then, the project “has achieved substantial popularity among the machine learning operations (MLOps) community” and is being adopted by companies across multiple industries, Lin writes. Ibrahim Haddad, the general manager of LF AI & Data, said in a press statement. Since launching Feathr internally in 2017, use of the software has grown. By defining the data features used in ML programs once in a common feature namespace, users can now pull them up “by name” from within ML workflows. [Feature Stores Emerging as Must-Have Tech for Machine Learning](https://www.datanami.com/2021/10/04/feature-stores-emerging-as-must-have-tech-for-machine-learning/) [AT&T and H2O.ai Team Up on Feature Store](https://www.datanami.com/2021/10/28/att-and-h2o-ai-team-up-on-feature-store/) [2021: The Year of the Feature Store](https://www.datanami.com/2021/01/19/2021-the-year-of-the-feature-store/) We look forward to the project’s continued growth and success as part of LF AI & Data.” [Artificial Intelligence](https://www.datanami.com/application/artificial-intelligence/) [Cloud](https://www.datanami.com/technology/cloud/), [Middleware](https://www.datanami.com/technology/middleware/) [big data](https://www.datanami.com/tag/big-data/), [Feathr](https://www.datanami.com/tag/feathr/), [feature pipeline](https://www.datanami.com/tag/feature-pipeline/), [feature store](https://www.datanami.com/tag/feature-store/), [machine learning](https://www.datanami.com/tag/machine-learning/), [MLOps](https://www.datanami.com/tag/mlops/) [Microsoft](https://www.microsoft.com/) is also part of the Feathr story (LinkedIn is owned by Microsoft). Feature stores also provide a more repeatable method for transforming source data into features (which is something not found in all feature stores), and boosts the performance of ML serving at the inference stage by centralizing the storage and serving of features. [LF AI & Data](https://lfaidata.foundation/) group, LinkedIn is putting additional governance in place around the open source project, which should help attract more users and more contributors to the project.