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    <title>topic Re: How to use databricks model serving endpoint with databricks application ? in Get Started Discussions</title>
    <link>https://community.databricks.com/t5/get-started-discussions/how-to-use-databricks-model-serving-endpoint-with-databricks/m-p/146674#M11389</link>
    <description>&lt;P&gt;Hi&amp;nbsp;&lt;a href="https://community.databricks.com/t5/user/viewprofilepage/user-id/112195"&gt;@gokkul&lt;/a&gt;&amp;nbsp;,&lt;/P&gt;&lt;P&gt;Check below databricks apps cookbook docs. The idea is to use databricks sdk inside databricks apps (or any other application) to invoke a model. So your app can ask user for an input and then pass it internally to serving_endpoints.query method.&lt;/P&gt;&lt;LI-CODE lang="python"&gt;import streamlit as st
from databricks.sdk import WorkspaceClient

w = WorkspaceClient()

response = w.serving_endpoints.query(
    name="custom-regression-model",
    dataframe_split={
        "columns": ["feature1", "feature2"],
        "data": [[1.5, 2.5]]
    }
)
st.json(response.as_dict())&lt;/LI-CODE&gt;&lt;P&gt;&lt;A href="https://apps-cookbook.dev/docs/streamlit/aiml/ml_serving_invoke/" target="_blank"&gt;Invoke a model | Databricks Apps Cookbook&lt;/A&gt;&lt;/P&gt;</description>
    <pubDate>Tue, 03 Feb 2026 07:05:10 GMT</pubDate>
    <dc:creator>szymon_dybczak</dc:creator>
    <dc:date>2026-02-03T07:05:10Z</dc:date>
    <item>
      <title>How to use databricks model serving endpoint with databricks application ?</title>
      <link>https://community.databricks.com/t5/get-started-discussions/how-to-use-databricks-model-serving-endpoint-with-databricks/m-p/146647#M11388</link>
      <description>&lt;P&gt;Hi DB Community ,&lt;/P&gt;&lt;P&gt;I have a databricks streamlit application that is viewing and inserting data into lakebase postgres database tables . Now I want to modernise this and implement databricks gen ai models . So instead of giving/selecting inputs , users can prompt what they want to insert . Prompt will be converted into sql query and data can be inserted .&amp;nbsp; So I have created a model serving endpoint . I want to know everything on how to implement this . Better if I could get any important code snippets .&lt;/P&gt;</description>
      <pubDate>Tue, 03 Feb 2026 04:08:11 GMT</pubDate>
      <guid>https://community.databricks.com/t5/get-started-discussions/how-to-use-databricks-model-serving-endpoint-with-databricks/m-p/146647#M11388</guid>
      <dc:creator>gokkul</dc:creator>
      <dc:date>2026-02-03T04:08:11Z</dc:date>
    </item>
    <item>
      <title>Re: How to use databricks model serving endpoint with databricks application ?</title>
      <link>https://community.databricks.com/t5/get-started-discussions/how-to-use-databricks-model-serving-endpoint-with-databricks/m-p/146674#M11389</link>
      <description>&lt;P&gt;Hi&amp;nbsp;&lt;a href="https://community.databricks.com/t5/user/viewprofilepage/user-id/112195"&gt;@gokkul&lt;/a&gt;&amp;nbsp;,&lt;/P&gt;&lt;P&gt;Check below databricks apps cookbook docs. The idea is to use databricks sdk inside databricks apps (or any other application) to invoke a model. So your app can ask user for an input and then pass it internally to serving_endpoints.query method.&lt;/P&gt;&lt;LI-CODE lang="python"&gt;import streamlit as st
from databricks.sdk import WorkspaceClient

w = WorkspaceClient()

response = w.serving_endpoints.query(
    name="custom-regression-model",
    dataframe_split={
        "columns": ["feature1", "feature2"],
        "data": [[1.5, 2.5]]
    }
)
st.json(response.as_dict())&lt;/LI-CODE&gt;&lt;P&gt;&lt;A href="https://apps-cookbook.dev/docs/streamlit/aiml/ml_serving_invoke/" target="_blank"&gt;Invoke a model | Databricks Apps Cookbook&lt;/A&gt;&lt;/P&gt;</description>
      <pubDate>Tue, 03 Feb 2026 07:05:10 GMT</pubDate>
      <guid>https://community.databricks.com/t5/get-started-discussions/how-to-use-databricks-model-serving-endpoint-with-databricks/m-p/146674#M11389</guid>
      <dc:creator>szymon_dybczak</dc:creator>
      <dc:date>2026-02-03T07:05:10Z</dc:date>
    </item>
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