diff --git a/examples/rest-api/stream_data.py b/examples/rest-api/stream_data.py
index 738d7314..95bbe463 100644
--- a/examples/rest-api/stream_data.py
+++ b/examples/rest-api/stream_data.py
@@ -2,28 +2,54 @@
 
 from openlayer import Openlayer
 
-# Prepare the config for the data, which depends on your project's task type. In this
-# case, we have an LLM project:
-from openlayer.types.inference_pipelines import data_stream_params
-
-# Let's say we want to stream the following row, which represents a model prediction:
-data = {"user_query": "what's the meaning of life?", "output": "42", "tokens": 7, "cost": 0.02, "timestamp": 1620000000}
-
 client = Openlayer(
     # This is the default and can be omitted
     api_key=os.environ.get("OPENLAYER_API_KEY"),
 )
 
-config = data_stream_params.ConfigLlmData(
-    input_variable_names=["user_query"],
-    output_column_name="output",
-    num_of_token_column_name="tokens",
-    cost_column_name="cost",
-    timestamp_column_name="timestamp",
-    prompt=[{"role": "user", "content": "{{ user_query }}"}],
-)
+# Let's say we want to stream the following row, which represents a tabular
+# classification model prediction, with features and a prediction:
+data = {
+    "CreditScore": 600,
+    "Geography": "France",
+    "Gender": "Male",
+    "Age": 42,
+    "Tenure": 5,
+    "Balance": 100000,
+    "NumOfProducts": 1,
+    "HasCrCard": 1,
+    "IsActiveMember": 1,
+    "EstimatedSalary": 50000,
+    "AggregateRate": 0.5,
+    "Year": 2020,
+    "Prediction": 1,
+}
 
+# Prepare the config for the data, which depends on your project's task type. In this
+# case, we have an Tabular Classification project:
+from openlayer.types.inference_pipelines import data_stream_params
+
+config = data_stream_params.ConfigTabularClassificationData(
+    categorical_feature_names=["Gender", "Geography"],
+    class_names=["Retained", "Exited"],
+    feature_names=[
+        "CreditScore",
+        "Geography",
+        "Gender",
+        "Age",
+        "Tenure",
+        "Balance",
+        "NumOfProducts",
+        "HasCrCard",
+        "IsActiveMember",
+        "EstimatedSalary",
+        "AggregateRate",
+        "Year",
+    ],
+    predictions_column_name="Prediction",
+)
 
+# Now, you can stream the data to the inference pipeline:
 data_stream_response = client.inference_pipelines.data.stream(
     inference_pipeline_id="YOUR_INFERENCE_PIPELINE_ID",
     rows=[data],