H2O AI Feature Store, currently in production use at AT&T, delivers a repository for collaborating, sharing, reusing and discovering machine learning features to speed AI project deployments, improve ROI and is now available to any company or organization
What’s the news? AT&T and H2O.ai jointly built an artificial intelligence (AI) feature store to manage and reuse data and machine learning engineering capabilities. The AI Feature Store houses and distributes the features data scientists, developers and engineers need to build AI models. The AI Feature Store is in production at AT&T, meeting the high levels of performance, reliability and scalability required to meet AT&T’s demand. Today, AT&T and H2O.ai are announcing that the same solution in production at AT&T, including all its industry-first capabilities, will now be available as the “H2O AI Feature Store” to any company or organization.
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What is a feature store? 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 (e.g., churn, likely to buy, demand forecasting). Building features is time consuming work, and typically data scientists build features from scratch every time they start a new project. 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 organization. Also, it is important that features are available for both training and real-time inference to avoid training-serving skew which causes model performance problems and contributes to project failure. Feature stores allow data scientists to build more accurate features and deploy these features in production in hours instead of months. Until now there weren’t places to store and access features from previous projects. As data and AI are and will continue to be important to every business, demand is growing to make these features reusable. Feature stores are seen as a critical component of the infrastructure stack for machine learning because they solve the hardest problem with operationalizing machine learning—building and serving machine learning data to production.
How is AT&T using its feature store? AT&T carries more than 465 petabytes of data traffic across its global network on an average day. When you add in the data generated internally from our different applications, in our stores, among our field technicians, and across other parts of our business, turning data into actionable intelligence as quickly as possible is vital to our success. AT&T’s implementation of the AI Feature Store has been instrumental in helping turn this massive trove of data into actionable intelligence.
Who will use the H2O AI Feature Store? We know other organizations feel the same way about making their own data actionable. H2O.ai, the leading AI cloud platform provider, has co-developed the feature store with us, and now together we are offering the production-tested feature store as a software platform for other companies and organizations to use with their own data. From financial services to health organizations and pharmaceutical makers, retail, software developers and more, we know the demand for reliable, easy-to-use, and secure feature stores is booming. Any organization currently using AI or planning to use AI will want to consider the value of a feature store. We expect customers to use the H2O AI Feature Store for forecasting, personalization and recommendation engines, dynamic pricing optimization, supply chain optimization, logistics and transportation optimization, and more. We are using the feature store at AT&T for network optimization, fraud prevention, tax calculations and predictive maintenance.
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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.
The H2O AI Feature Store 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.
What are people saying?
“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. “These storehouses are vital not only to our own work, but to other businesses, as well. With our expertise in managing and analyzing huge data flows, combined with H2O.ai’s deep AI expertise, we understand what business customers are looking for in this space and our Feature Store offering meets this need.”
“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 H2O.ai. “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. AI-powered feature stores focus on discoverability and reuse by automatically recommending highly predictive features to our customers using FeatureRank™. AT&T has built a world-class data and AI team and we are privileged to collaborate with them on their AI journey.”
