chore: import upstream snapshot with attribution

doc/source/serve/doc_code/sklearn_quickstart.py

@@ -0,0 +1,44 @@
+# flake8: noqa
+# fmt: off
+
+# __serve_example_begin__
+import requests
+from starlette.requests import Request
+from typing import Dict
+
+from sklearn.datasets import load_iris
+from sklearn.ensemble import GradientBoostingClassifier
+
+from ray import serve
+
+
+# Train model.
+iris_dataset = load_iris()
+model = GradientBoostingClassifier()
+model.fit(iris_dataset["data"], iris_dataset["target"])
+
+
+@serve.deployment
+class BoostingModel:
+    def __init__(self, model):
+        self.model = model
+        self.label_list = iris_dataset["target_names"].tolist()
+
+    async def __call__(self, request: Request) -> Dict:
+        payload = (await request.json())["vector"]
+        print(f"Received http request with data {payload}")
+
+        prediction = self.model.predict([payload])[0]
+        human_name = self.label_list[prediction]
+        return {"result": human_name}
+
+
+# Deploy model.
+serve.run(BoostingModel.bind(model), route_prefix="/iris")
+
+# Query it!
+sample_request_input = {"vector": [1.2, 1.0, 1.1, 0.9]}
+response = requests.get(
+    "http://localhost:8000/iris", json=sample_request_input)
+print(response.text)
+# __serve_example_end__