Iguazio announced that it has launched the first production-ready integrated feature store. The feature store, which sits at the heart of its data science platform, enables enterprises to catalogue, store and share features for development and deployment of AI in hybrid multi-cloud environments and is built to handle real-time use cases.

According to Gartner, one of the top barriers to AI implementation is the "complexity of AI solution(s) integrating with existing infrastructure". At the core of machine learning is the data, and operationalizing machine learning (MLOps) requires processing data at scale, building model-serving pipelines, and monitoring models for accuracy and drift. This is a long and resource-intensive effort.

Tech giants like Netflix, Twitter and Uber have already understood the inefficiency in this process and built their own feature stores to standardize the use of features across the organization and create a more efficient workflow. Iguazio is now bringing this capability to all enterprises, as a part of its platform.

"For companies that don't have hundreds of data scientists and data engineers, building a feature store from scratch, in-house, is not feasible," said Asaf Somekh, Co-Founder and CEO of Iguazio. "We wanted to bring this functionality to our customers, and provide them with an off-the-shelf solution for feature engineering across training, serving and monitoring in hybrid environments."

Uniquely, the Iguazio unified online and offline feature store, integrated within its data science platform, provides next-level automation of model monitoring and drift detection, enables training at scale, and running continuous integration and continuous delivery (CI/CD) of machine learning (ML). It plugs seamlessly into the data ingestion, model training, model serving, and model monitoring components of the platform. The feature store is built on Iguazio's open source MLOps framework, MLRun, enabling contributors to add data sources and contribute additional functionality.

Iguazio's platform is used by customers such as Payoneer, Quadient and Tulipan for various use cases such as fraud prediction and real-time recommendations. Earlier today, Iguazio also announced that it has entered into a strategic agreement with the Sheba Medical Center, the largest medical facility in Israel and the Middle East and ranked amongst the Top 10 Hospitals in the World by Newsweek magazine, to facilitate Sheba's transformation with AI. Clinical and logistical use cases include predicting and mitigating COVID-19 patient deterioration and optimizing patient journey with smart mobility.

"Using Iguazio, we are revolutionizing the way we use data, by unifying real-time and historic data from different sources and rapidly deploying and monitoring complex AI models to improve patient outcomes and the City of Health's efficiency", said Nathalie Bloch, MD, Head of Big Data & AI at Sheba Medical Center's ARC innovation complex

The solution has been embraced by Iguazio's strategic partners, including Microsoft Azure, NetApp and MongoDB, and regarded as an important accelerator to making the MLOps process of developing and deploying AI much simpler.

Boris Bialek, Global Head of Enterprise Modernization at MongoDB commented: "With Iguazio's feature store, MongoDB Atlas, our fully managed cloud database, can easily store features that are ready to use in machine and deep learning, making MLOps a reality. This refines the experience for both our advanced users, who are scaling AI, and those just starting out on their AI journey to innovate on top of an already powerful database."