# Mitigation

> Mitigation - 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 m

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 `2026-05-06T18:17:09.843373+00:00` (UTC).

## Primary page

- [Mitigation](https://docs.datarobot.com/en/docs/api/reference/sdk/tag-mitigation.html): Full documentation for this topic (HTML).

## Related documentation

- [Developer documentation](https://docs.datarobot.com/en/docs/api/index.html): Linked from this page.
- [API reference](https://docs.datarobot.com/en/docs/api/reference/index.html): Linked from this page.
- [Python API client](https://docs.datarobot.com/en/docs/api/reference/sdk/index.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.