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."