# Mitigation

> Mitigation - Manage the complex machine learning lifecycle to control the quality of models in
> production.

This Markdown file sits beside the HTML page at the same path (with a `.md` suffix). It summarizes the topic and lists links for tools and LLM context.

Companion generated at `2026-05-06T18:17:10.024331+00:00` (UTC).

## Primary page

- [Mitigation](https://docs.datarobot.com/en/docs/workbench/nxt-console/nxt-mitigation/index.html): Full documentation for this topic (HTML).

## Related documentation

- [NextGen UI documentation](https://docs.datarobot.com/en/docs/workbench/index.html): Linked from this page.
- [Console](https://docs.datarobot.com/en/docs/workbench/nxt-console/index.html): Linked from this page.
- [Challengers](https://docs.datarobot.com/en/docs/workbench/nxt-console/nxt-mitigation/nxt-challengers.html): Linked from this page.
- [Retraining](https://docs.datarobot.com/en/docs/workbench/nxt-console/nxt-mitigation/nxt-retraining.html): Linked from this page.
- [Humility](https://docs.datarobot.com/en/docs/workbench/nxt-console/nxt-mitigation/nxt-humility.html): Linked from this page.

## Documentation content

Machine learning models in production environments have a complex lifecycle; maintaining the predictive value of these models requires a robust and repeatable process to manage that lifecycle. Without proper management, models that reach production may deliver inaccurate data, poor performance, or unexpected results that can damage your business’s reputation for AI trustworthiness. Lifecycle management is essential for creating a machine learning operations system that allows you to scale many models in production.

| Topic | Description |
| --- | --- |
| Challengers | Compare model performance post-deployment. |
| Retraining | Define the retraining settings and then create retraining policies. |
| Humility | Monitor deployments to recognize, in real-time, when the deployed model makes uncertain predictions or receives data it has not seen before. |