diff --git a/README.md b/README.md
index 7e53da2e1..12fad21b1 100644
--- a/README.md
+++ b/README.md
@@ -10,7 +10,7 @@ Features:
 - Integrated with xformer, flash attention, rope scaling, and multipacking
 - Works with single GPU or multiple GPUs via FSDP or Deepspeed
 - Easily run with Docker locally or on the cloud
-- Log results and optionally checkpoints to wandb
+- Log results and optionally checkpoints to wandb or mlflow
 - And more!
 
 
@@ -695,6 +695,10 @@ wandb_name: # Set the name of your wandb run
 wandb_run_id: # Set the ID of your wandb run
 wandb_log_model: # "checkpoint" to log model to wandb Artifacts every `save_steps` or "end" to log only at the end of training
 
+# mlflow configuration if you're using it
+mlflow_tracking_uri: # URI to mlflow
+mlflow_experiment_name: # Your experiment name
+
 # Where to save the full-finetuned model to
 output_dir: ./completed-model
 
diff --git a/requirements.txt b/requirements.txt
index 391bb52d9..b2f5958de 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -22,6 +22,7 @@ hf_transfer
 colorama
 numba
 numpy>=1.24.4
+mlflow
 # qlora things
 bert-score==0.3.13
 evaluate==0.4.0
diff --git a/src/axolotl/cli/__init__.py b/src/axolotl/cli/__init__.py
index 4f441f527..15902516d 100644
--- a/src/axolotl/cli/__init__.py
+++ b/src/axolotl/cli/__init__.py
@@ -29,6 +29,7 @@ from axolotl.utils.config import normalize_config, validate_config
 from axolotl.utils.data import prepare_dataset
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.distributed import is_main_process
+from axolotl.utils.mlflow_ import setup_mlflow_env_vars
 from axolotl.utils.models import load_tokenizer
 from axolotl.utils.tokenization import check_dataset_labels
 from axolotl.utils.trainer import prepare_optim_env
@@ -289,6 +290,9 @@ def load_cfg(config: Path = Path("examples/"), **kwargs):
     normalize_config(cfg)
 
     setup_wandb_env_vars(cfg)
+
+    setup_mlflow_env_vars(cfg)
+
     return cfg
 
 
diff --git a/src/axolotl/core/trainer_builder.py b/src/axolotl/core/trainer_builder.py
index f0d1c4343..4c30fe517 100644
--- a/src/axolotl/core/trainer_builder.py
+++ b/src/axolotl/core/trainer_builder.py
@@ -747,7 +747,12 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             False if self.cfg.ddp else None
         )
         training_arguments_kwargs["group_by_length"] = self.cfg.group_by_length
-        training_arguments_kwargs["report_to"] = "wandb" if self.cfg.use_wandb else None
+        report_to = None
+        if self.cfg.use_wandb:
+            report_to = "wandb"
+        if self.cfg.use_mlflow:
+            report_to = "mlflow"
+        training_arguments_kwargs["report_to"] = report_to
         training_arguments_kwargs["run_name"] = (
             self.cfg.wandb_name if self.cfg.use_wandb else None
         )
diff --git a/src/axolotl/utils/mlflow_.py b/src/axolotl/utils/mlflow_.py
new file mode 100644
index 000000000..fec2028ba
--- /dev/null
+++ b/src/axolotl/utils/mlflow_.py
@@ -0,0 +1,18 @@
+"""Module for mlflow utilities"""
+
+import os
+
+from axolotl.utils.dict import DictDefault
+
+
+def setup_mlflow_env_vars(cfg: DictDefault):
+    for key in cfg.keys():
+        if key.startswith("mlflow_"):
+            value = cfg.get(key, "")
+
+            if value and isinstance(value, str) and len(value) > 0:
+                os.environ[key.upper()] = value
+
+    # Enable mlflow if experiment name is present
+    if cfg.mlflow_experiment_name and len(cfg.mlflow_experiment_name) > 0:
+        cfg.use_mlflow = True