Add README documentation for scikit-learn MNIST example

sdks/python/apache_beam/examples/inference/README.md

@@ -235,3 +235,54 @@ He looked up and saw the sun and stars .;moon
 Each line has data separated by a semicolon ";".
 The first item is the sentence with the last word masked. The second item
 is the word that the model predicts for the mask.
+
+---
+## MNITST digit classification
+[`sklearn_mnist_classification.py`](./sklearn_mnist_classification.py) contains
+an implementation for a RunInference pipeline that performs image classification on handwritten digits from the [MNIST](https://en.wikipedia.org/wiki/MNIST_database) database.
+
+The pipeline reads rows of pixels corresponding to a digit, performs basic preprocessing, passes the pixels to the Scikit-learn implementation of RunInference, and then writes the predictions to a text file.
+
+### Dataset and model for language modeling
+- **Required**: A path to a file called `INPUT` that contains label and pixels to
+feed into the model. Each row should have elements that are comma-separated. The first element is the label. All subsuequent values are pixels from pixel0 to pixel784. It should look something like this:
+```
+1,0,0,0...
+0,0,0,0...
+1,0,0,0...
+4,0,0,0...
+...
+```
+- **Required**: A path to a file called `OUTPUT`, to which the pipeline will
+write the predictions.
+- **Required**: A path to a file called `MODEL_PATH` that contains the pickled file of a scikit-learn model trained on MNIST data. Please refer to this scikit-learn [documentation](https://scikit-learn.org/stable/model_persistence.html) on how to serialize models.
+
+
+### Running `sklearn_mnist_classification.py`
+
+To run the MNIST classification pipeline locally, use the following command:
+```sh
+python -m apache_beam.examples.inference.sklearn_mnist_classification.py \
+  --input_file INPUT \
+  --output OUTPUT \
+  --model_path MODEL_PATH
+```
+For example:
+```sh
+python -m apache_beam.examples.inference.sklearn_mnist_classification.py \
+  --input_file mnist_data.csv \
+  --output predictions.csv \
+  --model_path mnist_model_svm.pickle
+```
+
+This writes the output to the `predictions.csv` with contents like:
+```
+1,1
+4,9
+7,1
+0,0
+...
+```
+Each line has data separated by a comma ",".
+The first item is the actual label of the digit. The second item
+is the predicted label of the digit.