Solutions · Model evaluation & validation

Validation data that holds your model to account

Test sets built to beat the model they measure, and human review of model output at production scale. For one AI validation platform: 600,000 labels reviewed and 50,000 created in a month, at 98% accuracy.

A person at a white test bench with a phone on a tripod and a laptop showing image thumbnails

The problem

Why this is hard to do well

01

Aggressive timelines

Evaluation lands right before launch, with no time to build a pipeline from scratch.

02

Quality targets

A test set has to be more accurate than the model it tests, or the scores mean nothing.

03

Errors hide in averages

One accuracy number hides the classes, languages, and conditions where a model fails.

How MLtwist does it

Platform and people, together

Our platform does the repeatable work; our team, yours, or both handle the judgment calls. Every version ships with a Data ID Card.

Golden test sets
Held-out data labeled and adjudicated by experts, versioned so every model is scored on the same set.
Human review of model output
Reviewers accept, correct, or reject model predictions, with the reason attached.
Measured by class
Agreement and miss rates by class and condition, so weak spots show up as numbers.
Fast turnaround
Out-of-the-box pipelines and built-in QC keep accuracy above target at full volume.

Talk to us about model evaluation & validation

Tell us the data type, volume, and timeline. We'll scope it with our team, yours, or both — and deliver it versioned, in your format.

Also available through Carahsoft and Google Cloud Marketplace.