ts/v0.2.0-rc0: Add `AddGuardrailModal` for creating guardrails (#22358)

The MLflow project, with 25.3k GitHub stars, has released version v0.2.0-rc0 featuring a significant new component: the `AddGuardrailModal`. This UI element (pull request #22358) provides teams with a dedicated interface for creating and configuring safety guardrails within their machine learning operations workflow. Guardrails are essential constraints that prevent AI models from generating harmful, biased, or unsafe outputs, and this modal makes implementing them more accessible to development teams.

Notably, this release demonstrates collaborative AI-assisted development, with the commit (aca3ebb) being co-authored by human engineers alongside Claude AI from Anthropic. The integration of AI co-authors in significant open-source projects like MLflow highlights the evolving nature of software development. For ML practitioners, this means they can now define safety parameters—such as content filters, output validators, and ethical boundaries—through a structured interface rather than manual code, streamlining responsible AI deployment.

The `AddGuardrailModal` represents MLflow's continued evolution from pure experiment tracking to comprehensive MLOps governance. By baking safety features directly into the platform, teams can enforce compliance and ethical standards earlier in the development lifecycle. This release candidate suggests MLflow is positioning itself as a central hub not just for model performance metrics, but for the entire responsible AI pipeline, from training to deployment with built-in safeguards.