Predicting AI model behavior in out-of-domain scenarios can be challenging, often leading to unexpected failures in unknown situations. Thorough testing and validation of AI models are essential for applications with real-world consequences, but this process differs significantly from traditional software testing. Additionally, upcoming government regulations on AI will emphasize safety and reliability, requiring demonstrable proof that thorough testing has been performed.
Zetane's Protector Platform enables testing numerous scenarios simultaneously, allowing users to observe model behavior and understand boundary conditions. By augmenting models with edge case data, the platform significantly improves model robustness and overall performance.
During testing, hard-to-debug inputs can be examined at a pixel level using Zetane's Insight Engine. This helps data scientists understand failure modes, increasing model accuracy, and catching critical failures before deployment.
Zetane's suite of tools, including Protector and Insight Engine, facilitate the development of trustworthy AI models, ensuring their safety and reliability in high-stake applications such as healthcare, finance, autonomous vehicles, and more.
By providing comprehensive testing and validation, Zetane ensures AI models meet stringent government regulations and industry standards.
Utilize Zetane’s Protector Platform to test multiple scenarios simultaneously.
Employ Zetane Insight Engine to observe hard-to-debug inputs at a pixel level.
Using Zetane's Protector Platform and Insight Engine, organizations can efficiently deploy safe and reliable AI solutions, meeting regulatory requirements, and ensuring trustworthy performance in real-world applications.
By leveraging Zetane's end-to-end platform, companies can confidently implement AI models, knowing they are thoroughly tested, validated, and capable of handling unknown scenarios with minimal risk.