Why insurance AI requires a training data platform
How insurance companies are putting ML to use
Common challenges ML teams face when creating and managing data for insurance use cases
Why AI teams in insurance need a Training Data Platform to mitigate those challenges effectively.
Example use cases of how insurance companies have improved ML efforts with a Training Data Platform
Why Labelbox?
Labelbox is the training data platform for production AI.
When we build software, we take for granted the existence of collaborative tools to write and debug code. The ML workflow has no standard tooling for labeling data, storing it, debugging models and then continually improving model accuracy.
We aim to change all that. Labelbox was built to help data science teams create high-quality training data and train neural networks with speed and ease-of-use.
Knowing feature counts and object analytics for your training data means informed decision making about the state of your model capabilities today and how to improve them.
Real-time insights at your fingertips
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Just some of companies that Labelbox is working with to build AI applications:
Machine learning teams at insurance companies are leveraging deep learning and convolutional neural network (CNN) models from the large amounts of data they collect for marketing, claims automation, risk assessment, and much more.
While insurance companies that employ AI have the potential to develop significant competitive advantages, the costs and uncertainties associated with creating high-quality training data often keep ML models from reaching their full potential.
Download Why insurance AI requires a training data platform to explore:
