[Tune] Rename MLFlow to MLflow (#13301)

doc/source/tune/_tutorials/tune-mlflow.rst

@@ -1,11 +1,11 @@
 .. _tune-mlflow:
 
-Using MLFlow with Tune
+Using MLflow with Tune
 ======================
 
-`MLFlow <https://mlflow.org/>`_ is an open source platform to manage the ML lifecycle, including experimentation,
+`MLflow <https://mlflow.org/>`_ is an open source platform to manage the ML lifecycle, including experimentation,
 reproducibility, deployment, and a central model registry. It currently offers four components, including
-MLFlow Tracking to record and query experiments, including code, data, config, and results.
+MLflow Tracking to record and query experiments, including code, data, config, and results.
 
 .. image:: /images/mlflow.png
   :height: 80px
@@ -13,33 +13,33 @@ MLFlow Tracking to record and query experiments, including code, data, config, a
   :align: center
   :target: https://www.mlflow.org/
 
-Ray Tune currently offers two lightweight integrations for MLFlow Tracking.
-One is the :ref:`MLFlowLoggerCallback <tune-mlflow-logger>`, which automatically logs
-metrics reported to Tune to the MLFlow Tracking API.
+Ray Tune currently offers two lightweight integrations for MLflow Tracking.
+One is the :ref:`MLflowLoggerCallback <tune-mlflow-logger>`, which automatically logs
+metrics reported to Tune to the MLflow Tracking API.
 
 The other one is the :ref:`@mlflow_mixin <tune-mlflow-mixin>` decorator, which can be
 used with the function API. It automatically
-initializes the MLFlow API with Tune's training information and creates a run for each Tune trial.
+initializes the MLflow API with Tune's training information and creates a run for each Tune trial.
 Then within your training function, you can just use the
-MLFlow like you would normally do, e.g. using ``mlflow.log_metrics()`` or even ``mlflow.autolog()``
+MLflow like you would normally do, e.g. using ``mlflow.log_metrics()`` or even ``mlflow.autolog()``
 to log to your training process.
 
 Please :doc:`see here </tune/examples/mlflow_example>` for a full example on how you can use either the
-MLFlowLoggerCallback or the mlflow_mixin.
+MLflowLoggerCallback or the mlflow_mixin.
 
-MLFlow AutoLogging
+MLflow AutoLogging
 ------------------
 You can also check out :doc:`here </tune/examples/mlflow_ptl_example>` for an example on how you can leverage MLflow
 autologging, in this case with Pytorch Lightning
 
-MLFlow Logger API
+MLflow Logger API
 -----------------
 .. _tune-mlflow-logger:
 
-.. autoclass:: ray.tune.integration.mlflow.MLFlowLoggerCallback
+.. autoclass:: ray.tune.integration.mlflow.MLflowLoggerCallback
    :noindex:
 
-MLFlow Mixin API
+MLflow Mixin API
 ----------------
 .. _tune-mlflow-mixin: