Update

Signed-off-by: Alyssa Goins <agoins@redhat.com>

Author: alyssacgoins

content/en/docs/components/pipelines/user-guides/core-functions/mlflow-plugin.md

@@ -20,21 +20,20 @@ As described directly above, these parameters and metrics are available through
 ## Configuring your KFP MLflow Plugin
 Pre-requisite: K8s cluster
 1. Following the official [MLflow Kubeflow Integration documentation](https://github.com/kubeflow/mlflow-integration) to configure your MLflow deployment.
-1. Install the MLflow operator (link) on your cluster.
 2. Configure your API config.json file with the following
 ```json
   "plugins": {
     "mlflow": {
-      "endpoint": "https://mlflow.opendatahub.svc.cluster.local:8443",
-      "timeout": "60s",
+      "endpoint": "https://<cluster-address>:8443",
+      "timeout": "<time in seconds>",
       "tls": {
         "insecureSkipVerify": false,
-        "caBundlePath": "/etc/mlflow-tracking-ca/ca.crt"
+        "caBundlePath": "<MLflow ca cert path>"
       },
       "settings": {
         "workspacesEnabled": true
       }
     }
   }
 ```
-3. Refer to the following documentation for deploying KFP here (add link)
+KFP runs will automatically be logged to the MLflow experiment specified in the API config.json file.