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The top-level namespace within Feast is a project. Users define one or more feature views within a project. Each feature view contains one or more features. These features typically relate to one or more entities. A feature view must always have a data source, which in turn is used during the generation of training datasets and when materializing feature values into the online store.
Projects provide complete isolation of feature stores at the infrastructure level. This is accomplished through resource namespacing, e.g., prefixing table names with the associated project. Each project should be considered a completely separate universe of entities and features. It is not possible to retrieve features from multiple projects in a single request. We recommend having a single feature store and a single project per environment (dev
, staging
, prod
).
Projects are currently being supported for backward compatibility reasons. Projects may change in the future as we simplify the Feast API.
In this tutorial we will
Deploy a local feature store with a Parquet file offline store and Sqlite online store.
Build a training dataset using our time series features from our Parquet files.
Materialize feature values from the offline store into the online store.
Read the latest features from the online store for inference.
You can run this tutorial in Google Colab or run it on your localhost, following the guided steps below.
In this tutorial, we use feature stores to generate training data and power online model inference for a ride-sharing driver satisfaction prediction model. Feast solves several common issues in this flow:
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 alerts users to offline / online skew with data quality monitoring
Online feature availability: At inference time, models often need access to features that aren't readily available and need to be precomputed from other datasources.
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.
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 discovery of and collaboration on previously used features and enables versioning of sets of features (via feature services).
Feast enables feature transformation so users can re-use transformation logic across online / offline usecases and across models.
Install the Feast SDK and CLI using pip:
In this tutorial, we focus on a local deployment. For a more in-depth guide on how to use Feast with Snowflake / GCP / AWS deployments, see Running Feast with Snowflake/GCP/AWS
Bootstrap a new feature repository using feast init
from the command line.
Let's take a look at the resulting demo repo itself. It breaks down into
data/
contains raw demo parquet data
example.py
contains demo feature definitions
feature_store.yaml
contains a demo setup configuring where data sources are
The key line defining the overall architecture of the feature store is the provider. This defines where the raw data exists (for generating training data & feature values for serving), and where to materialize feature values to in the online store (for serving).
Valid values for provider
in feature_store.yaml
are:
local: use file source with SQLite/Redis
gcp: use BigQuery/Snowflake with Google Cloud Datastore/Redis
aws: use Redshift/Snowflake with DynamoDB/Redis
Note that there are many other sources Feast works with, including Azure, Hive, Trino, and PostgreSQL via community plugins. See Third party integrations for all supported datasources.
A custom setup can also be made by following adding a custom provider.
The raw feature data we have in this demo is stored in a local parquet file. The dataset captures hourly stats of a driver in a ride-sharing app.
The apply
command scans python files in the current directory for feature view/entity definitions, registers the objects, and deploys infrastructure. In this example, it reads example.py
(shown again below for convenience) and sets up SQLite online store tables. Note that we had specified SQLite as the default online store by using the local
provider in feature_store.yaml
.