diff --git a/website/docs/mlflow/autologging.md b/website/docs/mlflow/autologging.md
new file mode 100644
index 0000000000..5703d03951
--- /dev/null
+++ b/website/docs/mlflow/autologging.md
@@ -0,0 +1,82 @@
+---
+title: SynapseML Autologging
+description: SynapseML autologging
+---
+
+## Automatic Logging
+
+[MLflow automatic logging](https://www.mlflow.org/docs/latest/tracking.html#automatic-logging) allows you to log metrics, parameters, and models without the need for explicit log statements.
+SynapseML supports autologging for every model in the library.
+
+To enable autologging for SynapseML:
+1. Download this customized [log_model_allowlist file](https://mmlspark.blob.core.windows.net/publicwasb/log_model_allowlist.txt) and put it at a place that your code has access to.
+For example:
+* In Synapse `wasb://<containername>@<accountname>.blob.core.windows.net/PATH_TO_YOUR/log_model_allowlist.txt`
+* In Databricks `/dbfs/FileStore/PATH_TO_YOUR/log_model_allowlist.txt`.
+2. Set spark configuration `spark.mlflow.pysparkml.autolog.logModelAllowlistFile` to the path of your `log_model_allowlist.txt` file. 
+3. Call `mlflow.pyspark.ml.autolog()` before your training code to enable autologging for all supported models.
+
+Note:
+1. If you want to support autologging of additional PySpark models not in the log_model_allowlist file, you can add models to the file.
+2. If you've enabled autologging, then please don't write explicit `with mlflow.start_run()` as it might cause multiple runs for one single model or one run for multiple models.
+
+
+## Configuration process in Databricks as an example
+
+1. Install latest MLflow via `%pip install mlflow -u`
+2. Upload your customized `log_model_allowlist.txt` file to dbfs by clicking File/Upload Data button on Databricks UI.
+3. Set Cluster Spark configuration following [this documentation](https://docs.microsoft.com/en-us/azure/databricks/clusters/configure#spark-configuration)
+```
+spark.mlflow.pysparkml.autolog.logModelAllowlistFile /dbfs/FileStore/PATH_TO_YOUR/log_model_allowlist.txt
+```
+4. Run the following before your training code, you can also customize corresponding [parameters](https://www.mlflow.org/docs/latest/python_api/mlflow.pyspark.ml.html#mlflow.pyspark.ml.autolog) by passing arguments to `autolog`.
+```
+mlflow.pyspark.ml.autolog()
+```
+5. To find your experiment's results via the `Experiments` tab of the MLFlow UI.
+<img src="https://mmlspark.blob.core.windows.net/graphics/adb_experiments.png" width="1200" />
+
+## Example for ConditionalKNNModel
+```python
+from pyspark.ml.linalg import Vectors
+from synapse.ml.nn import *
+
+df = spark.createDataFrame([
+    (Vectors.dense(2.0,2.0,2.0), "foo", 1),
+    (Vectors.dense(2.0,2.0,4.0), "foo", 3),
+    (Vectors.dense(2.0,2.0,6.0), "foo", 4),
+    (Vectors.dense(2.0,2.0,8.0), "foo", 3),
+    (Vectors.dense(2.0,2.0,10.0), "foo", 1),
+    (Vectors.dense(2.0,2.0,12.0), "foo", 2),
+    (Vectors.dense(2.0,2.0,14.0), "foo", 0),
+    (Vectors.dense(2.0,2.0,16.0), "foo", 1),
+    (Vectors.dense(2.0,2.0,18.0), "foo", 3),
+    (Vectors.dense(2.0,2.0,20.0), "foo", 0),
+    (Vectors.dense(2.0,4.0,2.0), "foo", 2),
+    (Vectors.dense(2.0,4.0,4.0), "foo", 4),
+    (Vectors.dense(2.0,4.0,6.0), "foo", 2),
+    (Vectors.dense(2.0,4.0,8.0), "foo", 2),
+    (Vectors.dense(2.0,4.0,10.0), "foo", 4),
+    (Vectors.dense(2.0,4.0,12.0), "foo", 3),
+    (Vectors.dense(2.0,4.0,14.0), "foo", 2),
+    (Vectors.dense(2.0,4.0,16.0), "foo", 1),
+    (Vectors.dense(2.0,4.0,18.0), "foo", 4),
+    (Vectors.dense(2.0,4.0,20.0), "foo", 4)
+], ["features","values","labels"])
+
+cnn = (ConditionalKNN().setOutputCol("prediction"))
+cnnm = cnn.fit(df)
+
+test_df = spark.createDataFrame([
+    (Vectors.dense(2.0,2.0,2.0), "foo", 1, [0, 1]),
+    (Vectors.dense(2.0,2.0,4.0), "foo", 4, [0, 1]),
+    (Vectors.dense(2.0,2.0,6.0), "foo", 2, [0, 1]),
+    (Vectors.dense(2.0,2.0,8.0), "foo", 4, [0, 1]),
+    (Vectors.dense(2.0,2.0,10.0), "foo", 4, [0, 1])
+], ["features","values","labels","conditioner"])
+
+display(cnnm.transform(test_df))
+```
+
+This should log one run with a ConditionalKNNModel artifact and its parameters.
+<img src="https://mmlspark.blob.core.windows.net/graphics/autologgingRunSample.png" width="1200" />
diff --git a/website/docs/mlflow/examples.md b/website/docs/mlflow/examples.md
index 13536674c5..6de06a9c0c 100644
--- a/website/docs/mlflow/examples.md
+++ b/website/docs/mlflow/examples.md
@@ -15,10 +15,10 @@ Install SynapseML based on the [installation guidance](../getting_started/instal
 
 ## API Reference
 
-[mlflow.spark.save_model](https://www.mlflow.org/docs/latest/python_api/mlflow.spark.html#mlflow.spark.save_model)
-[mlflow.spark.log_model](https://www.mlflow.org/docs/latest/python_api/mlflow.spark.html#mlflow.spark.log_model)
-[mlflow.spark.load_model](https://www.mlflow.org/docs/latest/python_api/mlflow.spark.html#mlflow.spark.load_model)
-[mlflow.log_metric](https://mlflow.org/docs/latest/python_api/mlflow.html#mlflow.log_metric)
+* [mlflow.spark.save_model](https://www.mlflow.org/docs/latest/python_api/mlflow.spark.html#mlflow.spark.save_model)
+* [mlflow.spark.log_model](https://www.mlflow.org/docs/latest/python_api/mlflow.spark.html#mlflow.spark.log_model)
+* [mlflow.spark.load_model](https://www.mlflow.org/docs/latest/python_api/mlflow.spark.html#mlflow.spark.load_model)
+* [mlflow.log_metric](https://mlflow.org/docs/latest/python_api/mlflow.html#mlflow.log_metric)
 
 ## LightGBMClassificationModel
 
diff --git a/website/sidebars.js b/website/sidebars.js
index a5a5b69f16..4e340d40de 100644
--- a/website/sidebars.js
+++ b/website/sidebars.js
@@ -122,6 +122,7 @@ module.exports = {
       items: [
         'mlflow/introduction',
         'mlflow/examples',
+        'mlflow/autologging'
       ],
     },
     {