diff --git a/doc/source/_toc.yml b/doc/source/_toc.yml
index 8ded7fee52121..ec11fb24ced65 100644
--- a/doc/source/_toc.yml
+++ b/doc/source/_toc.yml
@@ -16,6 +16,7 @@ parts:
       sections:
         - file: ray-air/preprocessors
         - file: ray-air/check-ingest
+        - file: ray-air/checkpoints
         - file: ray-air/examples/analyze_tuning_results
         - file: ray-air/examples/upload_to_comet_ml
         - file: ray-air/examples/upload_to_wandb
diff --git a/doc/source/ray-air/checkpoints.rst b/doc/source/ray-air/checkpoints.rst
new file mode 100644
index 0000000000000..b360b1917fe82
--- /dev/null
+++ b/doc/source/ray-air/checkpoints.rst
@@ -0,0 +1,84 @@
+Checkpoints
+===========
+
+Checkpoints are the common format for models that are used across different components of the Ray AI Runtime.
+
+.. image:: images/checkpoints.jpg
+
+What exactly is a checkpoint?
+-----------------------------
+
+The Checkpoint object is a serializable reference to a model. The model can represented in one of three ways:
+
+- a directory located on local (on-disk) storage
+- a directory located on external storage (e.g. cloud storage)
+- an in-memory dictionary
+
+The flexibility provided in the Checkpoint model representation is useful in distributed environments,
+where you may want to recreate the same model on multiple nodes in your Ray cluster for inference
+or across different Ray clusters.
+
+
+Creating a checkpoint
+---------------------
+
+There are two ways of generating a checkpoint.
+
+The first way is to generate it from a pretrained model. Each framework that AIR supports has a ``to_air_checkpoint`` method that can be used to generate an AIR checkpoint:
+
+.. literalinclude:: doc_code/checkpoint_usage.py
+    :language: python
+    :start-after: __checkpoint_quick_start__
+    :end-before: __checkpoint_quick_end__
+
+
+Another way is to retrieve it from the results of a Trainer or a Tuner.
+
+.. literalinclude:: doc_code/checkpoint_usage.py
+    :language: python
+    :start-after: __use_trainer_checkpoint_start__
+    :end-before: __use_trainer_checkpoint_end__
+
+What can I do with a checkpoint?
+--------------------------------
+
+Checkpoints can be used to instantiate a :class:`Predictor`, :class:`BatchPredictor`, or :class:`PredictorDeployment`.
+Upon usage, the model held by the Checkpoint will be instantiated in memory and used for inference.
+
+Below is an example using a checkpoint in the :class:`BatchPredictor` for scalable batch inference:
+
+.. literalinclude:: doc_code/checkpoint_usage.py
+    :language: python
+    :start-after: __batch_pred_start__
+    :end-before: __batch_pred_end__
+
+Below is an example using a checkpoint in a service for online inference via :class:`PredictorDeployment`:
+
+.. literalinclude:: doc_code/checkpoint_usage.py
+    :language: python
+    :start-after: __online_inference_start__
+    :end-before: __online_inference_end__
+
+The Checkpoint object has methods to translate between different checkpoint storage locations.
+With this flexibility, Checkpoint objects can be serialized and used in different contexts
+(e.g., on a different process or a different machine):
+
+.. literalinclude:: doc_code/checkpoint_usage.py
+    :language: python
+    :start-after: __basic_checkpoint_start__
+    :end-before: __basic_checkpoint_end__
+
+
+Example: Using Checkpoints with MLflow
+--------------------------------------
+
+MLflow has its own `checkpoint format <https://www.mlflow.org/docs/latest/models.html>`__ called the "MLflow Model". It is a standard format for packaging machine learning models that can be used in a variety of downstream tools.
+
+Below is an example of using MLflow models as a Ray AIR Checkpoint.
+
+.. literalinclude:: doc_code/checkpoint_mlflow.py
+    :language: python
+    :start-after: __mlflow_checkpoint_start__
+    :end-before: __mlflow_checkpoint_end__
+
+
diff --git a/doc/source/ray-air/doc_code/checkpoint_mlflow.py b/doc/source/ray-air/doc_code/checkpoint_mlflow.py
new file mode 100644
index 0000000000000..fd7a6af65b329
--- /dev/null
+++ b/doc/source/ray-air/doc_code/checkpoint_mlflow.py
@@ -0,0 +1,28 @@
+# flake8: noqa
+# isort: skip_file
+
+# __mlflow_checkpoint_start__
+from ray.air.checkpoint import Checkpoint
+from sklearn.ensemble import RandomForestClassifier
+import mlflow.sklearn
+
+# Create an sklearn classifier
+clf = RandomForestClassifier(max_depth=7, random_state=0)
+# ... e.g. train model with clf.fit()
+# Save model using MLflow
+mlflow.sklearn.save_model(clf, "model_directory")
+
+# Create checkpoint object from path
+checkpoint = Checkpoint.from_directory("model_directory")
+
+# Write it to some other directory
+checkpoint.to_directory("other_directory")
+# You can also use `checkpoint.to_uri/from_uri` to
+# read from/write to cloud storage
+
+# We can now use MLflow to re-load the model
+clf = mlflow.sklearn.load_model("other_directory")
+
+# It is guaranteed that the original data was recovered
+assert isinstance(clf, RandomForestClassifier)
+# __mlflow_checkpoint_end__
diff --git a/doc/source/ray-air/doc_code/checkpoint_usage.py b/doc/source/ray-air/doc_code/checkpoint_usage.py
new file mode 100644
index 0000000000000..c0bdc4181798f
--- /dev/null
+++ b/doc/source/ray-air/doc_code/checkpoint_usage.py
@@ -0,0 +1,130 @@
+# flake8: noqa
+# isort: skip_file
+
+# __checkpoint_quick_start__
+from ray.train.tensorflow import to_air_checkpoint
+import tensorflow as tf
+
+# This can be a trained model.
+def build_model() -> tf.keras.Model:
+    model = tf.keras.Sequential(
+        [
+            tf.keras.layers.InputLayer(input_shape=(1,)),
+            tf.keras.layers.Dense(1),
+        ]
+    )
+    return model
+
+
+model = build_model()
+
+checkpoint = to_air_checkpoint(model)
+# __checkpoint_quick_end__
+
+
+# __use_trainer_checkpoint_start__
+import pandas as pd
+import ray
+from ray.air import train_test_split
+from ray.train.xgboost import XGBoostTrainer
+
+
+bc_df = pd.read_csv(
+    "https://air-example-data.s3.us-east-2.amazonaws.com/breast_cancer.csv"
+)
+dataset = ray.data.from_pandas(bc_df)
+# Optionally, read directly from s3
+# dataset = ray.data.read_csv("s3://air-example-data/breast_cancer.csv")
+
+# Split data into train and validation.
+train_dataset, valid_dataset = train_test_split(dataset, test_size=0.3)
+
+trainer = XGBoostTrainer(
+    scaling_config={"num_workers": 2},
+    label_column="target",
+    params={
+        "objective": "binary:logistic",
+        "eval_metric": ["logloss", "error"],
+    },
+    datasets={"train": train_dataset},
+    num_boost_round=5,
+)
+
+result = trainer.fit()
+checkpoint = result.checkpoint
+# __use_trainer_checkpoint_end__
+
+# __batch_pred_start__
+from ray.train.batch_predictor import BatchPredictor
+from ray.train.xgboost import XGBoostPredictor
+
+# Create a test dataset by dropping the target column.
+test_dataset = valid_dataset.map_batches(
+    lambda df: df.drop("target", axis=1), batch_format="pandas"
+)
+
+batch_predictor = BatchPredictor.from_checkpoint(checkpoint, XGBoostPredictor)
+
+# Bulk batch prediction.
+batch_predictor.predict(test_dataset)
+# __batch_pred_end__
+
+
+# __online_inference_start__
+import requests
+from fastapi import Request
+import pandas as pd
+
+from ray import serve
+from ray.serve import PredictorDeployment
+from ray.serve.http_adapters import json_request
+
+
+async def adapter(request: Request):
+    content = await request.json()
+    print(content)
+    return pd.DataFrame.from_dict(content)
+
+
+serve.start(detached=True)
+deployment = PredictorDeployment.options(name="XGBoostService")
+
+deployment.deploy(
+    XGBoostPredictor, checkpoint, batching_params=False, http_adapter=adapter
+)
+
+print(deployment.url)
+
+sample_input = test_dataset.take(1)
+sample_input = dict(sample_input[0])
+
+output = requests.post(deployment.url, json=[sample_input]).json()
+print(output)
+# __online_inference_end__
+
+# __basic_checkpoint_start__
+from ray.air.checkpoint import Checkpoint
+
+# Create checkpoint data dict
+checkpoint_data = {"data": 123}
+
+# Create checkpoint object from data
+checkpoint = Checkpoint.from_dict(checkpoint_data)
+
+# Save checkpoint to a directory on the file system.
+path = checkpoint.to_directory()
+
+# This path can then be passed around,
+# # e.g. to a different function or a different script.
+# You can also use `checkpoint.to_uri/from_uri` to
+# read from/write to cloud storage
+
+# In another function or script, recover Checkpoint object from path
+checkpoint = Checkpoint.from_directory(path)
+
+# Convert into dictionary again
+recovered_data = checkpoint.to_dict()
+
+# It is guaranteed that the original data has been recovered
+assert recovered_data == checkpoint_data
+# __basic_checkpoint_end__
diff --git a/doc/source/ray-air/images/checkpoints.jpg b/doc/source/ray-air/images/checkpoints.jpg
new file mode 100644
index 0000000000000..3d868bf311c34
Binary files /dev/null and b/doc/source/ray-air/images/checkpoints.jpg differ
diff --git a/doc/source/ray-air/user-guides.rst b/doc/source/ray-air/user-guides.rst
index 98a6cf99cbad9..388048e467257 100644
--- a/doc/source/ray-air/user-guides.rst
+++ b/doc/source/ray-air/user-guides.rst
@@ -22,6 +22,15 @@ AIR Feature Guides
         :type: ref
         :text: How to use AIR Preprocessors?
         :classes: btn-link btn-block stretched-link
+    ---
+    :img-top: /ray-overview/images/ray_svg_logo.svg
+
+    +++
+    .. link-button:: /ray-air/checkpoints
+        :type: ref
+        :text: What are AIR Checkpoints?
+        :classes: btn-link btn-block stretched-link
+
 
     ---
     :img-top: /ray-overview/images/ray_svg_logo.svg
diff --git a/python/ray/air/checkpoint.py b/python/ray/air/checkpoint.py
index 8a0c830e7aec3..cb65d207dcdf3 100644
--- a/python/ray/air/checkpoint.py
+++ b/python/ray/air/checkpoint.py
@@ -33,7 +33,7 @@
 
 @PublicAPI(stability="alpha")
 class Checkpoint:
-    """Ray ML Checkpoint.
+    """Ray AIR Checkpoint.
 
     This implementation provides methods to translate between
     different checkpoint storage locations: Local storage, external storage