Why Multimodal Data Needs a Better Lakehouse? — Download the Research Study
Log in Sign up

Social media

Sign up

Geneva: Feature Engineering with LanceDB

💡 LanceDB Enterprise
Feature Engineering and the geneva package are fully supported as part of LanceDB Enterprise

geneva improves the productivity of AI engineers by streamlining feature engineering tasks. It is designed to reduce the time required to prototype, perform experiments, scale up, and move to production.

geneva uses Python User Defined Functions (UDFs) to define features as columns in a Lance dataset. Adding a feature is straightforward:

  1. Prototype your Python function in your favorite environment.
  2. Wrap the function with a small UDF decorator (see UDFs ).
  3. Register the UDF as a virtual column using Table.add_columns().
  4. Trigger a backfill operation (see Backfilling ).
💡 No setup or config required
You can build your Python feature generator function in an IDE or a notebook using your project’s Python versions and dependencies. geneva will automate much of the dependency and version management needed to move from prototype to scale and production.