Labelbox Nabs $25M Round
for AI Data Labeling
February 17, 2020
closed a $25 million Series B funding round led by Andreessen Horowitz
with General Partner Peter Levine joining the Labelbox board of
directors. Previous investors First Round Capital, Gradient Ventures
(Google’s AI-focused venture fund) and Kleiner Perkins also
participated. To date, Labelbox has raised $39 million in venture
Labelbox offers a training data platform for machine learning teams to
build real-world artificial intelligence. The platform consists of label
editor tools, batch & real-time labeling workflows, collaboration,
quality review, analytics, and an optional, fully managed and dedicated
labeling workforce. With state-of-the-art algorithms available for free
and AI computing costs drastically reducing, high-quality labeled
training data is the most valuable asset for enterprises adopting
supervised learning solutions. In less than two years, Labelbox has
become a foundational piece of infrastructure for more than one hundred
companies that are operating production-grade AI today.
The world is very quickly moving into a new paradigm where domain
experts directly teach artificial intelligence about the skilled work
they want it to do. This new paradigm is often called data-centric
programming or Software 2.0. Labelbox aims to become the de-facto
training data platform for Software 2.0, similar to what GitHub is for
Software 1.0. The Labelbox platform focuses today on computer vision
applications but can handle all forms of data.
Labelbox is defining a new category: training data platform. The
Labelbox product provides scalable tools, workflows, collaboration,
quality review, and automation to its customers on a unified platform.
Customers are taking control of their data and using various
out-of-the-box labeling tools and workflows to reliably build products
and services with AI. In addition, Labelbox is accelerating new AI
development across the enterprise because it serves as the company’s
central hub for all training data.
"If GitHub has become the platform for managing and developing software
code, then Labelbox has the potential to fill a similar role for data in
the AI/ML world," said Peter Levine, general partner at Andreessen
Horowitz. "We see Labelbox becoming the single source of truth for
defining, storing, and accessing training data across an entire
organization. We are thrilled to partner with company founders Manu,
Brian, Dan, and the team to help them realize their vision."
saw the opportunity to start Labelbox while working at Planet Labs, the
California company that photographs the earth from space and analyzes
more than seven terabytes of images daily in various ways. Engineers at
Planet Labs needed a software platform to create and manage training
data at scale and there was no off-the-shelf solution, so they built one
themselves. “A lot of this tooling was being built from scratch,” said
Sharma. “It seemed crazy to us that data scientists were building core
infrastructure in order to get started with AI.”
Sharma and his Labelbox co-founders realized Planet Labs wasn’t alone.
Every company building supervised learning models faced the same
problem. So they started Labelbox, an alternative to turning data over
to ‘black box’ labeling services where companies have no control.
Labelbox is currently being used by industries as diverse as agriculture
(where it is used to identify weeds in fields, for example), to
insurance (to spot risks), to sports analysis (to track the spin on
balls or the actions of players on the field), to healthcare
(identifying tumors or cells). With the new funding, Labelbox intends to
accelerate its roadmap and go to market. As supervised learning
transforms economies around the world, the opportunities for Labelbox
are seemingly endless.