Fast Track Shipping Insurance AI Models: Overcoming Training Data Challenges
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As the insurance sector undergoes rapid transformation, the advancement and competitiveness of AI and machine learning models are becoming crucial. However, complex data annotation challenges often obstruct the path to deploying powerful models. From navigating intricate labeling tasks to harnessing the expertise of Subject Matter Experts (SMEs) efficiently, the journey demands precision, innovation, and strategy.
Watch the webinar designed to propel your AI projects forward, reduce time-to-market, and ensure your models perform with unparalleled accuracy. Here's what you'll learn:
- Complex Labeling Simplified
- SME Integration & Automation
- Quality Datasets and Faster Deployment
Watch the replay
Paul Graffan
Solution Engineer at Kili Technology
More and more products are powered by machine learning. That’s why it's [capital] to think about ethics and to make sure [its] impact [is] positive.“
Clément Delangue,
Co-founder & CTO @Hugging Face
in Why Ethics are important in ML
“Our Data-centric approach to AI has helped us achieve a categorization of our own categories that is accurate in more than ninety-five percents of the videos.”
Andrea Colonna,
VP AI @Jellysmack
“But the real-world experience of those who put them into production shows that (...) it's often the quality of data (...) that makes your AI project succeed or fail.”
Edouard D'Archimbaud
Co-founder & CTO @Kili Technology
in Data labeling - Best practices for project management & collaboration
Labeling Platform for High-quality Data
One tool to label, find and fix issues, simplify DataOps,
and dramatically accelerate the build of reliable AI