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pipelines: document KFP MLflow plugin#4423

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pipelines: document KFP MLflow plugin#4423
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alyssacgoins:kfp-mlflow-integration-docs

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@alyssacgoins alyssacgoins commented Jul 6, 2026

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Description of Changes

Document the KFP MLflow integration implemented by kubeflow/pipelines#13441

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Skipping CI for Draft Pull Request.
If you want CI signal for your change, please convert it to an actual PR.
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@google-oss-prow
google-oss-prow Bot requested review from mprahl and rimolive July 6, 2026 15:30
@alyssacgoins alyssacgoins changed the title (docs): document KFP MLflow plugin pipelines: document KFP MLflow plugin Jul 6, 2026
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alyssacgoins force-pushed the kfp-mlflow-integration-docs branch from 2bc6283 to b8645af Compare July 8, 2026 14:25
@google-oss-prow google-oss-prow Bot added size/L and removed size/M labels Jul 8, 2026
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alyssacgoins force-pushed the kfp-mlflow-integration-docs branch from b8645af to 7d4660a Compare July 8, 2026 20:39
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alyssacgoins marked this pull request as ready for review July 8, 2026 20:42
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google-oss-prow Bot requested a review from hbelmiro July 8, 2026 20:42
Comment thread content/en/docs/components/pipelines/operator-guides/mlflow-plugin.md Outdated
- **tls**: TLS configuration for MLflow communication
- **insecureSkipVerify**: Set to `true` to skip TLS certificate verification (not recommended for production)
- **caBundlePath**: Path to the CA certificate for your MLflow server
- **settings**: See the [user guide](/docs/components/pipelines/user-guides/integrations/mlflow-plugin/) for a complete list of MLflow plugin settings

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Could you verify if the hyperlink is working ?

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Fixed

Comment thread content/en/docs/components/pipelines/operator-guides/mlflow-plugin.md Outdated

This integration bridges the gap between pipeline orchestration and experiment tracking, giving you a unified view of your machine learning workflows.
All tracked data is viewable in the MLflow UI, organized by experiments. You can specify a custom experiment name for each run, or use the default experiment configured by your administrator.
This page provides detailed instructions on how to set up the MLflow plugin for Kubeflow Pipelines at the user level, once it has been deployed and configured at the [admin level](/docs/components/pipelines/operator-guides/mlflow-plugin/).

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Could verify if the hyperlink is working?

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This link works successfully on my local deployment - did it fail for you?

- Deployed MLflow on your cluster
- Enabled and configured the KFP MLflow plugin

See the [operator guide](/docs/components/pipelines/operator-guides/mlflow-plugin/) for setup instructions.

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Could verify if the hyperlink is working?

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This link works successfully on my local deployment - did it fail for you?

@alyssacgoins
alyssacgoins force-pushed the kfp-mlflow-integration-docs branch from 7d4660a to 6eb5f0e Compare July 13, 2026 17:36
@alyssacgoins
alyssacgoins force-pushed the kfp-mlflow-integration-docs branch from 6eb5f0e to ee4551c Compare July 13, 2026 17:38

## User Configuration

To use the MLflow plugin with your pipelines, configure the plugin in your KFP API `config.json` file:

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I think this belongs in the operator/admin docs.

We can include the user level configuration on the kfp-launcher ConfigMap here for multiuser deployments, but I'd tend to put that in the operator/admin docs.


This integration bridges the gap between pipeline orchestration and experiment tracking, giving you a unified view of your machine learning workflows.
All tracked data is viewable in the MLflow UI, organized by experiments. You can specify a custom experiment name for each run, or use the default experiment configured by your administrator.
This page provides detailed instructions on how to set up the MLflow plugin for Kubeflow Pipelines at the user level, once it has been deployed and configured at the [admin level](/docs/components/pipelines/operator-guides/mlflow-plugin/).

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Could you include screenshots showing a run in KFP and a run in MLflow?


## Integration Overview

The KFP MLflow integration enables automatic experiment tracking for your Kubeflow Pipelines. When enabled, each pipeline run is automatically registered in MLflow, allowing you to:

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Could you mention the environment variable injection feature and point how a user may run MLflow's autolog (a link to MLflow docs for that would be good)?

weight = 1
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## MLflow & Experiment Tracking

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In general, this doc is a bit too admin focused. Could this be more focused on the end user that doesn't know anything about deploying/configuring KFP but wants to know how the MLflow integration works?

We should also link to the operator/admin guide in the prerequisites section.


You need an MLflow instance running on your Kubernetes cluster. One recommended option is the [OpenDataHub MLflow Operator](https://github.com/opendatahub-io/mlflow-operator#mlflow-operator), which simplifies MLflow deployment and management on Kubernetes.

To deploy using the MLflow Operator:

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We should not be documenting the MLflow operator as that is Red Hat specific.

weight = 4
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## KFP MLflow Plugin

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Could you make this admin guide more focused on the generic upstream MLflow installation (not the Red Hat or the soon to be Kubeflow specific deployment)?

I think it's only worth mentioning the mlflow-integration plugin when discussing the auth configuration and the RBAC requirements.

When using `bearer` or `basic-auth` authentication, this field references a Kubernetes secret containing the credentials.

#### workspacesEnabled
Enable MLflow workspaces for multi-tenant organization.

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Suggested change
Enable MLflow workspaces for multi-tenant organization.
Enable MLflow workspaces for multi-tenancy in organizations.


## Prerequisites

- Admin access to a Kubernetes cluster

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Suggested change
- Admin access to a Kubernetes cluster
- Administrative access to a Kubernetes cluster


- Admin access to a Kubernetes cluster
- Kubeflow Pipelines installed on your cluster
- kubectl configured to access your cluster

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Suggested change
- kubectl configured to access your cluster
- Kubectl configured to access your cluster

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