You can run this tutorial in Google Colab or run it on your localhost, following the guided steps below.

1. **Training-serving skew and complex data joins:** Feature values often exist across multiple tables. Joining these datasets can be complicated, slow, and error-prone.

* Feast joins these tables with battle-tested logic that ensures _point-in-time_ correctness so future feature values do not leak to models.

* Feast manages deployment to a variety of online stores (e.g. DynamoDB, Redis, Google Cloud Datastore) and ensures necessary features are consistently _available_ and _freshly computed_ at inference time.

3. **Feature reusability and model versioning:** Different teams within an organization are often unable to reuse features across projects, resulting in duplicate feature creation logic. Models have data dependencies that need to be versioned, for example when running A/B tests on model versions.

* Feast enables feature transformation so users can re-use transformation logic across online / offline usecases and across models.

{% tabs %}

{% tab title="Bash" %}