docs: add MLflow integration documentation to fine-tuning examples

- Add shared MLflow guide (examples/fine-tuning/mlflow.md) covering
  enabling the operator, creating the CR, and viewing experiments
- Link to the shared guide from lora, osft, and sft READMEs
- Add screenshots showing the Experiments page and run metrics
- Note that the KB article link requires Red Hat Customer Portal login

Co-authored-by: Cursor <cursoragent@cursor.com>

Author: briangallagher

examples/fine-tuning/images/mlflow-experiments.png

@@ -172,3 +172,7 @@ to seamlessly run fine-tuning jobs.
 >   You can skip the token if switching to non-gated models.
 
 You can now proceed with the instructions from the notebook. Enjoy!
+
+## MLflow Integration (Optional)
+
+The interactive notebook supports optional MLflow experiment tracking. See the [MLflow Integration guide](../mlflow.md) for setup instructions and details.

examples/fine-tuning/images/mlflow-run-metrics.png

@@ -0,0 +1,65 @@
+# MLflow Integration (Optional)
+
+Training Hub supports [MLflow](https://mlflow.org/) for experiment tracking. When MLflow is enabled on your RHOAI cluster, training metrics (loss, learning rate, etc.) are automatically logged to MLflow experiments — no additional code changes required beyond setting the experiment name.
+
+> [!NOTE]
+> MLflow integration is available for **interactive (single node)** notebooks only. Distributed training jobs do not currently support MLflow tracking.
+
+## Enabling MLflow
+
+Each interactive notebook already includes a cell that sets the MLflow experiment name:
+
+```python
+os.environ["MLFLOW_EXPERIMENT_NAME"] = "<your-experiment-name>"
+```
+
+For this to work, MLflow must be enabled as a component in your RHOAI installation. If MLflow is not enabled, the environment variable is simply ignored and training proceeds normally.
+
+**To enable MLflow on your cluster:**
+
+1. Enable the MLflow Operator component in your `DataScienceCluster` CR:
+
+   ```bash
+   oc patch datasciencecluster default-dsc \
+     --type=merge \
+     -p '{"spec":{"components":{"mlflowoperator":{"managementState":"Managed"}}}}'
+   ```
+
+2. Create an `MLflow` CR to deploy the tracking server (example using SQLite and a PV for storage):
+
+   ```bash
+   oc apply -f - <<EOF
+   apiVersion: mlflow.opendatahub.io/v1
+   kind: MLflow
+   metadata:
+     name: mlflow
+   spec:
+     backendStoreUri: "sqlite:////mlflow/mlflow.db"
+     defaultArtifactRoot: "file:///mlflow/artifacts"
+     serveArtifacts: true
+     storage:
+       accessModes:
+         - ReadWriteOnce
+       resources:
+         requests:
+           storage: 10Gi
+   EOF
+   ```
+
+For full details, see the [Configuring MLflow in OpenShift AI](https://access.redhat.com/articles/7136121) Knowledgebase article (requires Red Hat Customer Portal login).
+
+## Viewing MLflow Experiments
+
+Once training completes with MLflow enabled, you can browse your experiment runs:
+
+1. In the OpenShift AI dashboard, navigate to **Develop & train → Experiments** from the left sidebar menu.
+2. Select the experiment name to view all runs.
+3. Each run contains logged metrics (training loss, learning rate), parameters, and artifacts.
+
+You can also launch the full MLflow UI by clicking the **"Launch MLflow"** link in the top right of the Experiments page:
+
+![MLflow experiments page](./images/mlflow-experiments.png)
+
+Each run logs metrics including training loss, learning rate, samples per second, and more:
+
+![MLflow run metrics](./images/mlflow-run-metrics.png)

examples/fine-tuning/lora/README.md

@@ -207,3 +207,7 @@ These images serve both as training runtime and jupyter notebook images and come
 >   You can skip the token if switching to non-gated models.
 
 You can now proceed with the instructions from the notebook. Enjoy!
+
+## MLflow Integration (Optional)
+
+The interactive notebook supports optional MLflow experiment tracking. See the [MLflow Integration guide](../mlflow.md) for setup instructions and details.

examples/fine-tuning/mlflow.md

@@ -154,3 +154,7 @@ to seamlessly run fine-tuning jobs.
 >   You can skip the token if switching to non-gated models.
 
 You can now proceed with the instructions from the notebook. Enjoy!
+
+## MLflow Integration (Optional)
+
+The interactive notebook supports optional MLflow experiment tracking. See the [MLflow Integration guide](../mlflow.md) for setup instructions and details.