
Generative AI Software
DataLoop's data management and annotation platform streamlines the process of generating machine consumable datasets out of raw visual data.
- Revenue
- $3.5M
- Customers
- -
- Year founded
- 2017
- Funding
- $16M
- Team size
- 79
- Location
- Israel
Founded 2019
Find 4 competitors in the Generative AI Software. Compare other SaaS such as Dataloop AI, Scale AI and Labelbox. These orthodox.ai competitors have raised $1.8B and together serve more than 1.1K customers and employ over 6.9K team members.

Generative AI Software
DataLoop's data management and annotation platform streamlines the process of generating machine consumable datasets out of raw visual data.

Generative AI 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.

Generative AI 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.

Generative AI 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.
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