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THE PROBLEM

ML evaluation techniques are falling short.

Aggregate metrics don't tell the full story — unexpected model behavior in production is the norm.

Current testing processes are manual, error-prone, and unrepeatable. Models are evaluated on arbitrary statistical metrics that align imperfectly with product objectives.

Tracking model improvement over time as the data evolves is difficult and techniques sufficient in a research environment don't meet the demands of production.

There is a better way.

Our Solution

Ship high-quality models faster with a complete, end-to-end ML testing and debugging platform.

Our rigorous and systematic solution makes testing and comparing models efficient, repeatable, and inexpensive.

Ready to drastically improve your ML testing?

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