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Top Dataloop AI Alternatives, Competitors & Similar Software

Founded 2017

Find 3 competitors in the Data Labeling Software. Compare other SaaS such as Scale AI, Labelbox and Snorkel AI. These Dataloop AI competitors have raised $1.7B and together serve more than 1.1K customers and employ over 6.8K team members.

Compare Dataloop AI with Scale AI
1
Scale AI

Data Labeling Software

Scale AI Inc. is a machine learning data annotation platform that provides high-quality training data to help develop and improve artificial intelligence (AI) models. They offer a range of services including image and video annotation, natural language processing, and sensor fusion. Scale AI has received significant funding from investors and works with a number of high-profile clients in industries such as autonomous vehicles, robotics, and e-commerce.

Revenue
$2B
Customers
1K
Year founded
2016
Funding
$1.6B
Team size
5.8K
Location
United States
Compare Dataloop AI with Labelbox
2
Labelbox

Data Labeling Software

Developer of a data training platform intended for computer vision machine learning applications. The company's platform offers a visual workflow interface and system of record for the data labeling process, using annotation tools as well as quality control functionality and performance analytics, enabling business to reduces model development times and empowers data science teams to build great machine learning applications.

Revenue
$50M
Customers
50
Year founded
2018
Funding
-
Team size
232
Location
United States
Compare Dataloop AI with Snorkel AI
3
Snorkel AI

Data Labeling Software

Snorkel AI is a software company that provides a platform for building and managing training data for machine learning models. Their platform allows developers and data scientists to label data programmatically and efficiently, using a combination of weak supervision and human-in-the-loop labeling. By automating the data labeling process, Snorkel AI enables companies to train machine learning models faster and with higher accuracy, while reducing the cost and time required for data labeling. The company was founded in 2019 by a group of researchers from Stanford University and is based in Palo Alto, California.

Revenue
$148M
Customers
-
Year founded
2019
Funding
$135.3M
Team size
776
Location
United States
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