## Model Monitoring and Debugging

# Observe and Explain Your Models

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## Model Monitoring and Debugging with k-NN+

Howso’s model monitoring and debugging is particularly effective at drift monitoring. This is thanks to our k-NN+ approach and statistical techniques. k-NN+ means that we can add and remove cases rapidly, without expensive re-training operations or creating any number of new models. Additionally, Howso’s statistical measures for analyzing how surprising new data are can be used to detect and thoroughly characterize model drift.

## Live Data Connection

Howso can run alongside a traditional ML model and trained on the same training data or a subset of the training data. As data comes in, the surprisal of new data is measured and used to [validate](https://www.howso.com/validate/ "") if that data is different enough for model drift to be occurring. If this is detected, the model being monitored can be updated along with Howso to ensure that predictive power remains as high as possible.​

## Best-in-class Debugging

Howso can add its wide array of explanations to predictions made by the ML model. By reacting to production data and the prediction(s) made, Howso can provide highly accurate feature importances, residuals, and other explanations, including hypothetical values and boundary cases, that enable efficient data debugging.

## Experience 52% Faster Data Debugging

Howso improves the speed of model monitoring and debugging, saving you time and money.

## Ready to Unlock the Power of Model Monitoring and Data Debugging?

### Enhance Your Model Monitoring with k-NN+

k-NN+ offers a unique solution for model monitoring and debugging by allowing rapid addition and removal of cases without the need for expensive re-training or creating new models. This means that your monitored model can be updated in real-time, ensuring that its predictive power remains high and accurate. With k-NN+, you can easily adapt to changes in your data and optimize your model's performance quickly and efficiently.

Furthermore, k-NN+ enables best-in-class debugging capabilities by providing a wide array of explanations for predictions made by your ML model. By reacting to production data and predictions, k-NN+ can offer highly accurate feature importances, residuals, and other explanations that aid in efficient data debugging. With k-NN+, you can experience 52% faster data debugging, saving time and resources in the process.