[Python] Update tensorflow doc

CHANGES.md

@@ -67,7 +67,7 @@
 * Schema'd PTransforms can now be directly applied to Beam dataframes just like PCollections.
   (Note that when doing multiple operations, it may be more efficient to explicitly chain the operations
   like `df | (Transform1 | Transform2 | ...)` to avoid excessive conversions.)
-* The Go SDK adds new transforms periodic.Impulse and periodic.Sequence that extends support 
+* The Go SDK adds new transforms periodic.Impulse and periodic.Sequence that extends support
   for slowly updating side input patterns. ([#23106](https://github.com/apache/beam/issues/23106))
 
 ## Breaking Changes

website/www/site/content/en/documentation/ml/about-ml.md

@@ -112,14 +112,21 @@ that illustrates running Scikit-learn models with Apache Beam.
 
 #### TensorFlow
 
-To use TensorFlow with the RunInference API, you need to do the following:
-
-* Use `tfx_bsl` version 1.10.0 or later.
-* Create a model handler using `tfx_bsl.public.beam.run_inference.CreateModelHandler()`.
-* Use the model handler with the [`apache_beam.ml.inference.base.RunInference`](/releases/pydoc/current/apache_beam.ml.inference.base.html) transform.
-
-See [this notebook](https://github.com/apache/beam/blob/master/examples/notebooks/beam-ml/run_inference_tensorflow.ipynb)
-that illustrates running TensorFlow models with Apache Beam and tfx-bsl.
+To use TensorFlow with the RunInference API, you have two options:
+
+1. Use the built-in TensorFlow Model Handlers in Apache Beam SDK - `TFModelHandlerNumpy` and `TFModelHandlerTensor`.
+    * Depending on the type of input for your model, use `TFModelHandlerNumpy` for `numpy` input and `TFModelHandlerTensor` for `tf.Tensor` input respectively.
+    * Use tensorflow 2.7 or later.
+    * Pass the path of the model to the TensorFlow `ModelHandler` by using `model_uri=<path_to_trained_model>`.
+    * Alternatively, you can pass the path to saved weights of the trained model, a function to build the model using `create_model_fn=<function>`, and set the `model_type=ModelType.SAVED_WEIGHTS`.
+  See [this notebook](https://github.com/apache/beam/blob/master/examples/notebooks/beam-ml/run_inference_tensorflow_with_tensorflowhub.ipynb) that illustrates running Tensorflow models with Built-in model handlers.
+2. Using `tfx_bsl`.
+    * Use this approach if your model input is of type `tf.Example`.
+    * Use `tfx_bsl` version 1.10.0 or later.
+    * Create a model handler using `tfx_bsl.public.beam.run_inference.CreateModelHandler()`.
+    * Use the model handler with the [`apache_beam.ml.inference.base.RunInference`](/releases/pydoc/current/apache_beam.ml.inference.base.html) transform.
+  See [this notebook](https://github.com/apache/beam/blob/master/examples/notebooks/beam-ml/run_inference_tensorflow.ipynb)
+  that illustrates running TensorFlow models with Apache Beam and tfx-bsl.
 
 ## Custom Inference
 

website/www/site/content/en/documentation/sdks/python-machine-learning.md

@@ -120,14 +120,21 @@ that illustrates running Scikit-learn models with Apache Beam.
 
 ### TensorFlow
 
-To use TensorFlow with the RunInference API, you need to do the following:
-
-* Use `tfx_bsl` version 1.10.0 or later.
-* Create a model handler using `tfx_bsl.public.beam.run_inference.CreateModelHandler()`.
-* Use the model handler with the [`apache_beam.ml.inference.base.RunInference`](/releases/pydoc/current/apache_beam.ml.inference.base.html) transform.
-
-See [this notebook](https://github.com/apache/beam/blob/master/examples/notebooks/beam-ml/run_inference_tensorflow.ipynb)
-that illustrates running TensorFlow models with Apache Beam and tfx-bsl.
+To use TensorFlow with the RunInference API, you have two options:
+
+1. Use the built-in TensorFlow Model Handlers in Apache Beam SDK - `TFModelHandlerNumpy` and `TFModelHandlerTensor`.
+    * Depending on the type of input for your model, use `TFModelHandlerNumpy` for `numpy` input and `TFModelHandlerTensor` for `tf.Tensor` input respectively.
+    * Use tensorflow 2.7 or later.
+    * Pass the path of the model to the TensorFlow `ModelHandler` by using `model_uri=<path_to_trained_model>`.
+    * Alternatively, you can pass the path to saved weights of the trained model, a function to build the model using `create_model_fn=<function>`, and set the `model_type=ModelType.SAVED_WEIGHTS`.
+  See [this notebook](https://github.com/apache/beam/blob/master/examples/notebooks/beam-ml/run_inference_tensorflow_with_tensorflowhub.ipynb) that illustrates running Tensorflow models with Built-in model handlers.
+2. Using `tfx_bsl`.
+    * Use this approach if your model input is of type `tf.Example`.
+    * Use `tfx_bsl` version 1.10.0 or later.
+    * Create a model handler using `tfx_bsl.public.beam.run_inference.CreateModelHandler()`.
+    * Use the model handler with the [`apache_beam.ml.inference.base.RunInference`](/releases/pydoc/current/apache_beam.ml.inference.base.html) transform.
+  See [this notebook](https://github.com/apache/beam/blob/master/examples/notebooks/beam-ml/run_inference_tensorflow.ipynb)
+  that illustrates running TensorFlow models with Apache Beam and tfx-bsl.
 
 ## Use custom models