Data Quality for Machine Learning
Machine learning model verification is essentially a type of data pipeline. Once a model is trained, a common question is, “How does this model perform vs a previous version of this model?”
Validator can help ensure the consistency of both the input to the model and the model output as it corresponds to the model’s inputs.
You can send Validator a variety of data types for inputs, and send data from multiple stages, ensuring that “preprocessing” into your model also behaves correctly. This rich, multi-stage data capture enables not only rapid model verification but also verification of the supporting code around the model’s deployment.
Interested in trying Validator out in your machine learning environment? Write to us at firstname.lastname@example.org.