Add README

elibixby · elibixby · commit a241ff27df69 · 2017-02-27T14:35:54.000-08:00
diff --git a/ml_engine/online_prediction/README.md b/ml_engine/online_prediction/README.md
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+# Online Prediction with the Cloud Machine Learning Engine
+
+This sample assumes that you have already run the Cloud ML Engine end to end walkthrough using the UCI Census dataset, including training a wide & deep model and deploying it into production, ready to take prediction requests.
+
+This sample then shows you how to create a client that takes user JSON inputs, and sends those inputs as online prediction requests to a given deployed model.
+
+In order to use this client, first obtain the following information, and store it into the given environment variables:
+
+```
+PROJECT=<your project name>
+MODEL=<your model name>
+VERSION=<version of model you are using>
+```
+
+Next, launch this client as follows:
+
+```
+python predict.py --project=$PROJECT --model=$MODEL --version=$VERSION
+```
+
+After having done that, the client will ask you for ‘Valid JSON’ input as follows:
+
+```
+Valid JSON >>>
+```
+
+Now you can input a JSON example that corresponds to the schema of the given model. For instance if you are sending prediction requests to the census-based model created in the Cloud ML Engine walkthrough, you can send a JSON example like the following:
+
+```
+{"age": 25, "workclass": " Private", "education": " 11th", "education_num": 7, "marital_status": " Never-married", "occupation": " Machine-op-inspct", "relationship": " Own-child", "race": " Black", "gender": " Male", "capital_gain": 0, "capital_loss": 0, "hours_per_week": 40, "native_country": " United-States"}
+```
+The result should be something along the following lines (depending on how you trained the model/what parameters you used, the results may vary):
+
+```
+[{u'probabilities': [0.992774486541748, 0.007225471083074808], u'logits': [-4.922891139984131], u'classes': 0, u'logistic': [0.007225471083074808]}]
+```
+
+Now that you have a working client, you can adapt this to your own use cases!
