Keras 3.0 Logging With MLflow (#10415)

Signed-off-by: Jesse Chan <jesse.chan@databricks.com>
mlflow · Dec 3, 2023 · d10f342 · d10f342
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diff --git a/docs/source/deep-learning/index.rst b/docs/source/deep-learning/index.rst
@@ -74,7 +74,31 @@ The officially supported integrations for deep learning libraries in MLflow enco
     </section>
 
 Harness the power of these integrations and elevate your deep learning projects with MLflow's comprehensive support.
+For detailed guide on how to integrate MLflow with these libraries, refer to the following pages:
 
+.. raw:: html
+
+    <section>
+        <article class="simple-grid">
+            <div class="simple-card">
+                <a href="keras/index.html">
+                    <div class="header">
+                        Keras
+                    </div>
+                    <p>
+                        Learn about MLflow's native integration with the Keras library and see example notebooks that leverage
+                        MLflow and Keras to build deep learning workflows.
+                    </p>
+                </a>
+            </div>
+        </article>
+    </section>
+
+.. toctree::
+    :maxdepth: 1
+    :hidden:
+
+    keras/index
 
 MLflow Tracking for Deep Learning
 ---------------------------------

diff --git a/docs/source/deep-learning/keras/index.rst b/docs/source/deep-learning/keras/index.rst
@@ -0,0 +1,39 @@
+MLflow Keras 3.0 Integration
+============================
+
+Introduction
+------------
+
+Keras is a deep learning API written in Python, running on top of the machine learning platform TensorFlow. 
+It was developed with a focus on enabling fast experimentation.
+
+Keras 3.0 (Keras Core) makes it possible to run Keras workflows on top of TensorFlow, JAX, and PyTorch. 
+It also enables you to seamlessly integrate Keras components (like layers, models, or metrics) as part of 
+low-level TensorFlow, JAX, and PyTorch workflows.
+
+MLflow provides built-in support for Keras 3.0 workflows. It provides a callback that allows you to 
+log parameters and metrics during model training. Model logging is not currently supported.
+
+5 Minute Quick Start with MLflow + Keras 3.0
+--------------------------------------------
+
+To get a quick overview of how to use MLflow + Keras 3.0, please read the quickstart guide. It will walk
+you through how to use the callback for tracking experiments, as well as how to customize it.
+
+.. raw:: html
+
+    <a href="quickstart/quickstart_keras_core.html" class="download-btn">View the Quickstart</a>
+
+To download the Keras 3.0 tutorial notebook to run in your environment, click the link below:
+
+.. raw:: html
+
+    <a href="https://raw.githubusercontent.com/mlflow/mlflow/master/docs/source/deep-learning/keras/quickstart/quickstart_keras_core.ipynb"
+    class="notebook-download-btn">Download the Quickstart of MLflow Keras Integration</a><br>
+
+
+.. toctree::
+    :maxdepth: 1
+    :hidden:
+
+    quickstart/quickstart_keras_core.ipynb
diff --git a/docs/source/deep-learning/keras/quickstart/quickstart_keras_core.ipynb b/docs/source/deep-learning/keras/quickstart/quickstart_keras_core.ipynb
diff --git a/mlflow/sklearn/__init__.py b/mlflow/sklearn/__init__.py
@@ -165,7 +165,7 @@ def save_model(
     metadata=None,
 ):
     """
-    Save a scikit-learn model to a path on the local file system. Produces an MLflow Model
+    Save a scikit-learn model to a path on the local file system. Produces a MLflow Model
     containing the following flavors:
 
         - :py:mod:`mlflow.sklearn`