Databricks Feature Store provides a centralized repository that enables data scientists to find and share features and also ensures that the same code used to compute the feature values is used for model training and inference.

Databricks Feature Store solves the complexity of handling both big data sets at scale for training and small data for real-time inference, accelerating your data science team with best practices.

In this demo, we will cover the full Feature Store and Online Table capabilities in a set of three notebooks. Each notebook will introduce new capabilities:

• Feature Store lookup tables within Unity Catalog
• Leverage Databricks AutoML to programmatically build a model
• Use point-in-time lookups to prevent data leakage
• Add a streaming table to refresh your features in real-time
• Deploy an online store for real-time inference
• Add Feature Spec to compute features in realtime through Unity Catalog function
• Deploy your model as a serverless serving endpoint

To install the demo, get a free Databricks workspace and execute the following two commands in a Python notebook

%pip install dbdemos

import dbdemos
dbdemos.install('feature-store', catalog='main', schema='dbdemos_fs_travel')

Dbdemos is a Python library that installs complete Databricks demos in your workspaces. Dbdemos will load and start notebooks, Delta Live Tables pipelines, clusters, Databricks SQL dashboards, warehouse models … See how to use dbdemos

Dbdemos is distributed as a GitHub project.

For more details, please view the GitHub README.md file and follow the documentation.
Dbdemos is provided as is. See the License and Notice for more information.
Databricks does not offer official support for dbdemos and the associated assets.
For any issue, please open a ticket and the demo team will have a look on a best-effort basis.