Add docs on pre/post processing operations and dlq support (#26772)

* Add docs on pre/post processing operations and dlq support

* Apply suggestions from code review

Co-authored-by: Rebecca Szper <98840847+rszper@users.noreply.github.com>

* Add description to RunInference Patterns

* Wording

Co-authored-by: Rebecca Szper <98840847+rszper@users.noreply.github.com>

---------

Co-authored-by: Rebecca Szper <98840847+rszper@users.noreply.github.com>

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

@@ -192,7 +192,12 @@ with pipeline as p:
 
 For more information on resource hints, see [Resource hints](/documentation/runtime/resource-hints/).
 
-## Use a keyed ModelHandler
+## RunInference Patterns
+
+This section suggests patterns and best practices that you can use to make your inference pipelines simpler,
+more robust, and more efficient.
+
+### Use a keyed ModelHandler
 
 If a key is attached to the examples, wrap the `KeyedModelHandler` around the `ModelHandler` object:
 
@@ -212,7 +217,7 @@ If you are unsure if your data is keyed, you can also use `MaybeKeyedModelHandle
 
 For more information, see [`KeyedModelHander`](https://beam.apache.org/releases/pydoc/current/apache_beam.ml.inference.base.html#apache_beam.ml.inference.base.KeyedModelHandler).
 
-## Use the `PredictionResult` object
+### Use the `PredictionResult` object
 
 When doing a prediction in Apache Beam, the output `PCollection` includes both the keys of the input examples and the inferences. Including both these items in the output allows you to find the input that determined the predictions.
 
@@ -245,12 +250,7 @@ from apache_beam.ml.inference.base import PredictionResult
 
 For more information, see the [`PredictionResult` documentation](https://github.com/apache/beam/blob/master/sdks/python/apache_beam/ml/inference/base.py#L65).
 
-## Run a machine learning pipeline
-
-For detailed instructions explaining how to build and run a Python pipeline that uses ML models, see the
-[Example RunInference API pipelines](https://github.com/apache/beam/tree/master/sdks/python/apache_beam/examples/inference) on GitHub.
-
-## Automatic model refresh
+### Automatic model refresh
 To automatically update the models used with the RunInference `PTransform` without stopping the Beam pipeline, pass a [`ModelMetadata`](https://beam.apache.org/releases/pydoc/current/apache_beam.ml.inference.base.html#apache_beam.ml.inference.base.ModelMetadata) side input `PCollection` to the RunInference input parameter `model_metadata_pcoll`.
 
 `ModelMetdata` is a `NamedTuple` containing:
@@ -267,7 +267,65 @@ The side input `PCollection` must follow the [`AsSingleton`](https://beam.apache
 **Note**: If the main `PCollection` emits inputs and a side input has yet to receive inputs, the main `PCollection` is buffered until there is
             an update to the side input. This could happen with global windowed side inputs with data driven triggers, such as `AfterCount` and `AfterProcessingTime`. Until the side input is updated, emit the default or initial model ID that is used to pass the respective `ModelHandler` as a side input.
 
-## Beam Java SDK support
+### Preprocess and postprocess your records
+
+With RunInference, you can add preprocessing and postprocessing operations to your transform.
+To apply preprocessing operations, use `with_preprocess_fn` on your model handler:
+
+```
+inference = pcoll | RunInference(model_handler.with_preprocess_fn(lambda x : do_something(x)))
+```
+
+To apply postprocessing operations, use `with_postprocess_fn` on your model handler:
+
+```
+inference = pcoll | RunInference(model_handler.with_postprocess_fn(lambda x : do_something_to_result(x)))
+```
+
+You can also chain multiple pre- and postprocessing operations:
+
+```
+inference = pcoll | RunInference(
+    model_handler.with_preprocess_fn(
+      lambda x : do_something(x)
+    ).with_preprocess_fn(
+      lambda x : do_something_else(x)
+    ).with_postprocess_fn(
+      lambda x : do_something_after_inference(x)
+    ).with_postprocess_fn(
+      lambda x : do_something_else_after_inference(x)
+    ))
+```
+
+The preprocessing function is run before batching and inference. This function maps your input `PCollection`
+to the base input type of the model handler. If you apply multiple preprocessing functions, they run on your original
+`PCollection` in the order of last applied to first applied.
+
+The postprocessing function runs after inference. This function maps the output type of the base model handler
+to your desired output type. If you apply multiple postprocessing functions, they run on your original
+inference result in the order of first applied to last applied.
+
+### Handle errors while using RunInference
+
+To handle errors robustly while using RunInference, you can use a _dead-letter queue_. The dead-letter queue outputs failed records into a separate `PCollection` for further processing.
+This `PCollection` can then be analyzed and sent to a storage system, where it can be reviewed and resubmitted to the pipeline, or discarded.
+RunInference has built-in support for dead-letter queues. You can use a dead-letter queue by applying `with_exception_handling` to your RunInference transform:
+
+```
+main, other = pcoll | RunInference(model_handler).with_exception_handling()
+other.failed_inferences | beam.Map(print) # insert logic to handle failed records here
+```
+
+You can also apply this pattern to RunInference transforms with associated pre- and postprocessing operations:
+
+```
+main, other = pcoll | RunInference(model_handler.with_preprocess_fn(f1).with_postprocess_fn(f2)).with_exception_handling()
+other.failed_preprocessing[0] | beam.Map(print) # handles failed preprocess operations, indexed in the order in which they were applied
+other.failed_inferences | beam.Map(print) # handles failed inferences
+other.failed_postprocessing[0] | beam.Map(print) # handles failed postprocess operations, indexed in the order in which they were applied
+```
+
+### Run inference from a Java pipeline
 
 The RunInference API is available with the Beam Java SDK versions 2.41.0 and later through Apache Beam's [Multi-language Pipelines framework](/documentation/programming-guide/#multi-language-pipelines). For information about the Java wrapper transform, see [RunInference.java](https://github.com/apache/beam/blob/master/sdks/java/extensions/python/src/main/java/org/apache/beam/sdk/extensions/python/transforms/RunInference.java). To try it out, see the [Java Sklearn Mnist Classification example](https://github.com/apache/beam/tree/master/examples/multi-language). Additionally, see [Using RunInference from Java SDK](https://beam.apache.org/documentation/ml/multi-language-inference/) for an example of a composite Python transform that uses the RunInference API along with preprocessing and postprocessing from a Beam Java SDK pipeline.