AT&T and join forces to create new Feature Store

AT&T and have launched a co-developed artificial intelligence Feature Store, which includes ‘industry-first capabilities’

AT&T, a telecom giant, and AI startup have teamed up to co-develop a feature store to manage and reuse data and machine learning engineering capabilities.

The jointly developed H2O AI Feature Store will be available for any company to help better manage their data science development and production efforts. The AI Feature Storehouses and distributes the features data scientists, developers and engineers need to build AI models. 


How can a Feature Store help? 

Data scientists and AI experts use data engineering tools to create “features,” which are a combination of relevant data and derived data that predict an outcome. Building features is time-consuming work, and typically data scientists build features from scratch every time they start a new project.

According to AT&T, data scientists and AI experts spend up to 80% of their time on feature engineering, and because teams do not have a way to share this work, the same work is repeated by teams throughout the organisation. 

Instead of starting from scratch to figure out which data features or metrics are the most important or predictive for a given machine learning project, the data scientist or ML engineer can look in the feature store to see what features worked in the past, and then re-use those features for the new application.

“Feature stores are one of the hottest areas of AI development right now, because being able to reuse and repurpose data engineering tools is critical as those tools become increasingly complex and expensive to build,” said Andy Markus, Chief Data Officer, AT&T. 


What does the Feature Store include? 

The H2O AI Feature Store includes industry-first capabilities, including integration with multiple data and machine learning pipelines, which can be applied to an on-premise data lake or by leveraging cloud and SaaS providers.

It also includes Automatic Feature Recommendations, an industry first, which let data scientists select the features they want to update and improve and receive recommendations to do so. The H2O AI Feature Store recommends new features and feature updates to improve the AI model performance. The data scientists review the suggested updates and accept the recommendations they want to include

“Data is a team sport and collaboration with domain experts is key to discovering and sharing features. Feature Stores are the digital ‘water coolers’ for data science,” said Sri Ambati, CEO and founder of “We are building AI right into the Feature Store and have taken an open, modular and scalable approach to tightly integrate into the diverse feature engineering pipelines while preserving sub-millisecond latencies needed to react to fast-changing business conditions.”



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