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Iguazio raises $24m to bring its Data Science Platform to new markets

by Max Smolaks
Article Image

Banking on the growing popularity of MLOps

by Max
Smolaks
29 January
2020

Israeli
startup Iguazio, which develops a data science platform capable of processing
information in real-time, has raised another $24 million in venture capital.

The funding
package comes from a combination of new and existing investors, led by INCapital
Ventures, and brings its total funding amount to $72m.

Iguazio’s
platform enables customers to build machine learning pipelines; it can aggregate
data from multiple sources and uses Kubernetes for scaling.

“Customers
are using our platform to build a pipeline that uses data efficiently, because
at the end of the day, collecting data is a headache: you want to collect data
from your real-time operational systems, collect historical data that is relevant
to the users that are now transacting, put all of that into the model and do it
in parallel will all the other users of the platform,” Asaf Somekh, co-founder
and CEO of Iguazio, told AI Business. “What we enable people to do is to build
that pipeline in an automatic way.”

Iguazio will spend the additional cash on
accelerating the growth of the platform and expanding its reach to new markets.

The startup has also announced that it
has signed up Payoneer, a payment company with some four million users, as its latest
customer.

For
the ops

Iguazio
was established in 2014 to create a platform that would simplify deployment of
machine learning at scale, while outperforming cloud-based tools from major
vendors like AWS, Microsoft and Google.

Its data
science platform fits into the emerging category of MLOps software, focused on the
practical aspects of managing machine learning models.

MLOps
tools are supposed to serve as a bridge between data scientists who develop the
models in a lab environment, and operations teams that are tasked with supporting
those models in production using corporate IT infrastructure.

“There’s
a lot of hype, a lot of work going on around PoCs. And in many cases, the work
in the lab where the data scientists have the data they need is very, very
successful, they come up with great AI applications, with high accuracy models,”
Somekh said.

“And
then comes the hard part: taking that into production. This is where there is a
very big disconnect between the perceptions of the people and the real world.

“People
underestimate the complexity of taking the work that they’ve done in a lab and
bringing it to life.”

Iguazio’s
platform can be deployed on-premises, in the cloud, or on edge devices. Somekh
said that roughly half of the company’s customers were in financial services, but
its tools have also been used to create self-healing networks, and optimize ride-hailing
services, logistics, and real-time recommendation engines.

The latest
business to get on board with Iguazio is Payoneer, a developer of a global
payment platform for business. It has used the automated pipeline to build
fraud prevention capability that features predictive machine learning models,
served in real-time.

“We’ve
tackled one of our most elusive challenges with real-time predictive models, making
fraud attacks almost impossible on Payoneer,” said Yaron Weiss, VP Corporate
Security and Global IT Operations at Payoneer.

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