"Thanks to Raymon we get alerts when our data or model predictions are not of the quality we expect, and find out why easily."


The moment you put a model in production
it starts degrading

Machine learning is not train once, deploy forever. Your model's quality depends on its input data quality, which is liable to change or break down in production. It is essential to monitor your data and model quality to prevent silent quality degradation and value loss.

Monitor model
prediction quality
Track data drift
& integrity
Monitor subset performance
Know where
to improve

Deploy models with confidence

Data and model prediction quality monitoring.

Raymon helps you set up data quality and model quality monitoring with a few lines of code. We offer out-of-the-box metrics for structural, vision and time series data, and allow you to define your own. 

Model health

Alerts and dashboards.

Raymon provides default dashboards tracking all relevant metrics and alerts you when your data or model quality drops, globally or for subsets of your data.  

Troubleshooting and dataset building.

Raymon allows you to easily slice-and-dice your dashboards, metrics and predictions to debug issues. Raymon also helps you inspect specific predictions and find valuable data to improve your models.


Keep calm and improve

Raymon helps you rest assured you meet claimed prediction performance and helps you to continuously improve your models. With our extensive troubleshooting and dataset building functionalities we boost productivity and reduce costs of supportive tooling.

Prevent silent breakdowns

Ensure continuous business value

Surface areas of poor performance

Curate valuable data slices

We're flexible

Any location

Runs on premise or in your VPC so you stay in control of your data. We don't need it, we don't want it.

any location

Any model

We're currently compatible with any Python based system. Use your favourite tools!

Any model