Overview
Superb AI Platform provides automation-based modules for data labeling, QA, curation and model training and deployment. These modules are designed to enable computer vision teams to create efficiencies across the full MLOps lifecycle for the purpose of rapid model building and deployment.
Highlights
- Conveniently perform all major steps of the machine learning lifecycle in one platform : Curate, Label and Model.
- Curate : Auto-curate and build datasets that are well balanced, representative and most optimal for model performance. Teams can also leverage this technology to surface edge cases, mislabels and dataset imbalances. *Label : Manage large-scale data labeling projects with cloud-based project management functionalities and labeling tools that cover all data and annotation types for computer vision. Quickly build in-platform auto-label models that are specific to any use case. Use these models to l
- Get access to an expert computer vision team that can manage your project from start to finish from data curation and labeling to model training and deployment. Utilize a global network of data labeling providers that are uniquely sourced for every project based on needs of the customer.
Details
Features and programs
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Pricing
Dimension | Description | Cost/12 months |
---|---|---|
Enterprise plan | 200K Label Data, 200K Curate Data, 200K AI Credit / a year | $40,000.00 |
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Software as a Service (SaaS)
SaaS delivers cloud-based software applications directly to customers over the internet. You can access these applications through a subscription model. You will pay recurring monthly usage fees through your AWS bill, while AWS handles deployment and infrastructure management, ensuring scalability, reliability, and seamless integration with other AWS services.
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Customer reviews
The most unique, versatile and complete annotation platform I've ever used!
Great platform to serve a variety of Data Annotation needs and excellent team to cooperate with
More than Data Labeling, as standard as ‘import pandas’ for AI/ML projects
Less DevOps and Enginnering time means more resources available to focus on model development.
It was great but the lagging made it frustrating.
Excellent UX with the right features to accelerate labeling for computer vision
The labeling management tools such as team features are excellent. The ability to build auto label models on labeled data is extremely useful to accelerate labeling.