Scale AI’s Series C: Building the data platform to accelerate machine learning
Our mission at Scale is to accelerate the development of AI applications.
We’re proud of what we’ve built over the last three years, and today we’re
announcing our Series C funding round to support our continued work against
that mission.
Scale has raised $100m at a valuation of over $1B.
Founders Fund led the round, supported
by Accel,
Mike Krieger and
We are big believers in the transformational impact of AI over the coming
decades. Machine learning is likely the most important technological shift of
our time, and the overall benefits to the world will be comparable to those of
the internet.
What we noticed after working on AI at some of the most advanced organizations
in the world was that building machine learning systems was challenging due to
a lack of mature infrastructure. In particular, we noticed that the critical
bottleneck to further progress today was data—in particular, labeled datasets.
Many engineers will tell you that getting labeled data is the hardest part of
building a machine learning model.
Building AI represents a fundamentally different paradigm than building
traditional software. The performance of an AI system depends more on the data
than the algorithm—in most cases, without labeled data, there is no model.
Similarly, for a model to improve and adapt, it requires more data rather than
simply more code. This paradigm has been true since the very beginning of deep
learning; the modern deep learning age was ignited by the launch of the
ImageNet dataset by Fei-Fei Li’s lab at Stanford in 2009.
We set out to solve this problem by building a platform that enables
organizations with large high-quality datasets needed to build AI systems.
Especially now, as AI is being applied in more and more ways, this matters
because:
- Safe, accurate and unbiased AI systems depend on large volumes of high
- quality training data.
- The process to acquire, label, and verify training data is slow, manual and
- expensive.
- Today’s training data bottleneck limits AI's impact to a small group of
- well-funded technology companies.
Scale is a solution to these problems. Developers use our API to send their
raw data to our platform. We then use a combination of machine learning and
human insight to label and annotate this data. We pass this high-quality
ground truth data back to our customers, who are then enabled to build
world-class models to solve real-world problems.
Scale is now used by world leading machine learning teams at companies like
OpenAI, Airbnb, Nuro, and Lyft. Our platform is being used to provide
high-quality training data to an ever growing number of machine learning teams
and industries, including:
- Autonomous Vehicles: Advancements in AI for perception,
- prediction, and planning have enabled the development of autonomy, which in
- turn relies on data.
- Natural Language and Content Understanding: More than ever
- before, systems can understand and drive decision-making on large corpuses
- of speech, text, images, and other forms of content.
- E-commerce and Search: Powerful search systems that connect
- consumers to specific content, images, or products increasingly rely on AI
- systems to drive understanding of this content. Our platform helps engineers
- train those systems.
- Mapping: Mapping using satellite, drone, and street-level
- imagery for use cases from agriculture to insurance rely on high quality
- training data to ensure accuracy.
- Robotics: Robots in factory or warehouse settings need to
- be able to understand their environments, safely interact with humans and
- recognize products or components.
- Augmented Reality and Virtual Reality: AR and VR systems
- used in gaming, real estate or manufacturing rely on being able to perceive
- the 3D environment around a camera, headset or user.
- Offline retail: Cashierless checkouts, inventory management
- systems and footfall analysis all depend on perception systems, which in
- turn depend on training data.
We’re proud of the progress we’ve made building the Scale platform for our
customers. It’s been an incredibly quick 3 years since I started the company,
and the ability we’ve had to accelerate progress in machine learning is
palpable. But, as we hope is always the case, we’re far more excited about
what we will do in the future.
There is a lack of mature infrastructure for building AI, forcing teams to
build their own technology stack from scratch before they can have a
meaningful impact. Per our mission, we know there are many problems beyond the
training data bottleneck for us to solve to continue accelerating AI
development. Despite our excitement for the potential of AI, we’re closer to
the beginning of its history than we are to the end.
We’re grateful to our customers for building world-leading AI products with
Scale, and we’re looking forward to accelerating the development of machine
learning in an even broader set of industries and technologies as we grow. If
you are interested in being part of creating the infrastructure machine
learning needs, take a look at our
If you're interested in partnering with us, please