<?xml version="1.0" encoding="UTF-8"?>
<rss xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:taxo="http://purl.org/rss/1.0/modules/taxonomy/" version="2.0">
  <channel>
    <title>topic Offline Feature Store in Databricks Serving in Machine Learning</title>
    <link>https://community.databricks.com/t5/machine-learning/offline-feature-store-in-databricks-serving/m-p/136072#M4366</link>
    <description>&lt;P&gt;Hi,&amp;nbsp;&lt;/P&gt;&lt;P&gt;I am planning to deploy a model&amp;nbsp;(pyfunc)&amp;nbsp; with Databricks Serving. During inference, my model needs to retrieve some data from delta tables. I could make these tables to an offline feature store as well.&lt;/P&gt;&lt;P&gt;Latency is not so important. It doesnt matter too much if it is not milliseconds retrieval latency but in the seconds so I dont really want to go with an online feature store as this will not be worth the extra cost.&lt;/P&gt;&lt;P&gt;What would be my best options to get data from the delta tables?&lt;/P&gt;&lt;P&gt;Thanks!&lt;/P&gt;</description>
    <pubDate>Sun, 26 Oct 2025 13:51:51 GMT</pubDate>
    <dc:creator>AlexH</dc:creator>
    <dc:date>2025-10-26T13:51:51Z</dc:date>
    <item>
      <title>Offline Feature Store in Databricks Serving</title>
      <link>https://community.databricks.com/t5/machine-learning/offline-feature-store-in-databricks-serving/m-p/136072#M4366</link>
      <description>&lt;P&gt;Hi,&amp;nbsp;&lt;/P&gt;&lt;P&gt;I am planning to deploy a model&amp;nbsp;(pyfunc)&amp;nbsp; with Databricks Serving. During inference, my model needs to retrieve some data from delta tables. I could make these tables to an offline feature store as well.&lt;/P&gt;&lt;P&gt;Latency is not so important. It doesnt matter too much if it is not milliseconds retrieval latency but in the seconds so I dont really want to go with an online feature store as this will not be worth the extra cost.&lt;/P&gt;&lt;P&gt;What would be my best options to get data from the delta tables?&lt;/P&gt;&lt;P&gt;Thanks!&lt;/P&gt;</description>
      <pubDate>Sun, 26 Oct 2025 13:51:51 GMT</pubDate>
      <guid>https://community.databricks.com/t5/machine-learning/offline-feature-store-in-databricks-serving/m-p/136072#M4366</guid>
      <dc:creator>AlexH</dc:creator>
      <dc:date>2025-10-26T13:51:51Z</dc:date>
    </item>
    <item>
      <title>Re: Offline Feature Store in Databricks Serving</title>
      <link>https://community.databricks.com/t5/machine-learning/offline-feature-store-in-databricks-serving/m-p/136075#M4367</link>
      <description>&lt;P&gt;There is a ready feature engineering function for that:&amp;nbsp;&lt;/P&gt;
&lt;LI-CODE lang="python"&gt;# on non ML runtime please install databricks-feature-engineering&amp;gt;=0.13.0a3"

from databricks.feature_engineering import FeatureEngineeringClient
fe = FeatureEngineeringClient()

from databricks.feature_engineering import FeatureLookup

# The `FeatureSpec` can be accessed in Unity Catalog as a function.
# `FeatureSpec`s can be used to create training sets or feature serving endpoints.
fe.create_feature_spec(
  name = f"{CATALOG}.{SCHEMA}.feature_spec",
  features=[
    FeatureLookup(
      table_name=f"{CATALOG}.{SCHEMA}.offline_feature_table",
      lookup_key="id",
    ),
  ],
)

## add serving endpoint (can be done through UI too)
rom databricks.feature_engineering.entities.feature_serving_endpoint import (
  ServedEntity,
  EndpointCoreConfig,
)

fe.create_feature_serving_endpoint(
  name="my-feature-serving-endpoint",
  config=EndpointCoreConfig(
    served_entities=ServedEntity(
      feature_spec_name=f"{CATALOG}.{SCHEMA}.feature_spec",
        workload_size="Small",
        scale_to_zero_enabled=True,
        instance_profile_arn=None,
    )
  )
)

## inference



import mlflow.deployments

client = mlflow.deployments.get_deploy_client("databricks")

response = client.predict(
    endpoint="my-feature-serving-endpoint",
    inputs={
        "dataframe_records": [
            {"id": 1},
            {"id": 7},
            {"id": 12345},
        ]
    },
)
print(response)
     
&lt;/LI-CODE&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;</description>
      <pubDate>Sun, 26 Oct 2025 14:08:22 GMT</pubDate>
      <guid>https://community.databricks.com/t5/machine-learning/offline-feature-store-in-databricks-serving/m-p/136075#M4367</guid>
      <dc:creator>Hubert-Dudek</dc:creator>
      <dc:date>2025-10-26T14:08:22Z</dc:date>
    </item>
    <item>
      <title>Re: Offline Feature Store in Databricks Serving</title>
      <link>https://community.databricks.com/t5/machine-learning/offline-feature-store-in-databricks-serving/m-p/136077#M4368</link>
      <description>&lt;P&gt;Thanks. That is helpful already.&amp;nbsp;&lt;BR /&gt;Is this working without an online feature store?&lt;/P&gt;&lt;P&gt;In the docs it reads like this is based on online feature stores:&amp;nbsp;&lt;A href="https://learn.microsoft.com/en-us/azure/databricks/machine-learning/feature-store/feature-function-serving" target="_blank"&gt;https://learn.microsoft.com/en-us/azure/databricks/machine-learning/feature-store/feature-function-serving&lt;/A&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;</description>
      <pubDate>Sun, 26 Oct 2025 14:14:58 GMT</pubDate>
      <guid>https://community.databricks.com/t5/machine-learning/offline-feature-store-in-databricks-serving/m-p/136077#M4368</guid>
      <dc:creator>AlexH</dc:creator>
      <dc:date>2025-10-26T14:14:58Z</dc:date>
    </item>
  </channel>
</rss>

