Take the Uncertainty Out of Neural Networks
Forget About
Zero Visibility.
Unreliable Models.
Tons of Experiments.
Long Development Cycles.
Failures in Production.
Forget About
Zero Visibility. Unreliable Models. Tons of Experiments. Long Development Cycles. Failures in Production.
Zero Visibility. Unreliable Models. Tons of Experiments. Long Development Cycles. Failures in Production.
It’s time to remove the blindfold!
The Only Debugging and Explainability Platform for Deep Learning
For Data Scientists
For Organizations
Gain Clarity and Insight
Understand how your model interprets the data, detect root cause of failures, fix edge cases fast, and slash the number of experiments
Boost Reliability
Pinpoint, resolve, and test all populations and verify that the model relies on the right features
Balance Datasets
Identify and remove irrelevant data, eliminate bottlenecks, and only label what’s needed
The Visibility You Need to Build Models You Can Trust
Understand Model Failures
Identify and fix problems fast with unsupervised root cause detection
Focus Only on Data that Counts
Build unbiased datasets by removing irrelevant samples and prioritizing labeling
Identify Which Model to Deploy and Why
Instantly verify and validate thousands of data populations with deep unit testing
Track & Share Iterations Across the Team
Make informed decisions with clear documentation of the development process
