ML Testing and Debugging the Easy Way

Is Your Machine Learning Performance Held Back by Weak Model Testing?

Many enterprises struggle to achieve high-performing outcomes for their AI models, and some have faced serious consequences from poor model quality. Well-publicized examples of massive ML failures include incorrectly forecasting housing prices during rapid market shifts, erroneous credit scores leading to unwarranted consumer loan denial, and biased recruiting resulting in gender discrimination.

Model testing and evaluation are key to improving AI quality and ML performance, but it's often a messy process. While the challenges can be many, there are three that top the rest:

• Model debugging takes too long and requires a lot of manual work
• Limited testing is done in development, with no systematic process
• Lack of transparency and explainability slows down approvals

It’s clear that a new approach is necessary! Read this whitepaper to learn about the latest advances in strategy and tools for improving ML performance. 

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