diff --git a/README.md b/README.md
index aebf68900..15b7bc3f2 100644
--- a/README.md
+++ b/README.md
@@ -823,12 +823,13 @@ For details about how you can use set a custom stopping criteria and perform cus
 
 ## Experiment Tracking
 
-Experiment tracking in fms-hf-tuning allows users to track their experiments with known trackers like [Aimstack](https://aimstack.io/) or custom trackers built into the code like
+Experiment tracking in fms-hf-tuning allows users to track their experiments with known trackers like [Aimstack](https://aimstack.io/), [MLflow](https://mlflow.org/) or custom trackers built into the code like
 [FileLoggingTracker](./tuning/trackers/filelogging_tracker.py)
 
 The code supports currently two trackers out of the box, 
 * `FileLoggingTracker` : A built in tracker which supports logging training loss to a file.
 * `Aimstack` : A popular opensource tracker which can be used to track any metrics or metadata from the experiments.
+* `MLflow` : Another popular opensource tracker which stores metrics, metadata or even artifacts from experiments.
 
 Further details on enabling and using the trackers mentioned above can be found [here](docs/experiment-tracking.md).  
 
diff --git a/docs/experiment-tracking.md b/docs/experiment-tracking.md
index edc4e5978..deefdd35e 100644
--- a/docs/experiment-tracking.md
+++ b/docs/experiment-tracking.md
@@ -115,6 +115,34 @@ sft_trainer.train(train_args=training_args, tracker_configs=tracker_configs,....
 The code expects either the `local` or `remote` repo to be specified and will result in a `ValueError` otherwise.
 See [AimConfig](https://github.com/foundation-model-stack/fms-hf-tuning/blob/a9b8ec8d1d50211873e63fa4641054f704be8712/tuning/config/tracker_configs.py#L25) for more details.
 
+## MLflow Tracker
+
+To enable [MLflow](https://mlflow.org/) users need to pass `"mlflow"` as the requested tracker as part of the [training argument](https://github.com/foundation-model-stack/fms-hf-tuning/blob/a9b8ec8d1d50211873e63fa4641054f704be8712/tuning/config/configs.py#L131).
+
+
+When using MLflow, users need to specify additional arguments which specify [mlflow tracking uri](https://mlflow.org/docs/latest/tracking.html#common-setups) location where either a [mlflow supported database](https://mlflow.org/docs/latest/tracking/backend-stores.html#supported-store-types) or [mlflow remote tracking server](https://mlflow.org/docs/latest/tracking/server.html) is running.
+
+Example
+```
+from tuning import sft_trainer
+from tuning.config.tracker_configs import MLflowConfig, TrackerConfigFactory
+
+training_args = TrainingArguments(
+    ...,
+    trackers = ["mlflow"],
+)
+
+tracker_configs = TrackerConfigFactory(
+    mlflow_config=MLflowConfig(
+        mlflow_experiment="experiment-name",
+        mlflow_tracking_uri=<tracking uri>
+        )
+    )
+
+sft_trainer.train(train_args=training_args, tracker_configs=tracker_configs,....)
+```
+
+The code expects a valid uri to be specified and will result in a `ValueError` otherwise.
 
 ## Running the code via command line `tuning/sft_trainer::main` function
 
@@ -123,10 +151,10 @@ If running the code via main function of [sft_trainer.py](../tuning/sft_trainer.
 To enable tracking please pass
 
 ```
---tracker <aim/file_logger>
+--tracker <aim/file_logger/mlflow>
 ```
 
-To further customise tracking you can specify additional arguments needed by the tracker like
+To further customise tracking you can specify additional arguments needed by the tracker like (example shows aim follow similarly for mlflow)
 
 ```
 --tracker aim --aim_repo <path-to-aimrepo> --experiment <experiment-name>