Feature View User Guides#

A feature view is a query over feature groups plus the metadata a model needs to read it consistently. These guides cover creating one, reading training and inference data, and keeping the two aligned.

Start here

Select features from one or more feature groups and save the selection as a feature view.

query = trans_fg.select_all().join(profile_fg.select(["age"]))
fv = fs.get_or_create_feature_view(
    name="transactions_fraud",
    version=1,
    query=query,
    labels=["fraud_label"],
)

Create a feature view · Training data · Feature vectors

Create

Create a feature view Select, join, filter and label, then save a version.
Query Joins, filters and point-in-time correctness.
Helper columns Columns for training or inference logic that are not model inputs.
Spines Bring your own keys and labels at read time.
Model-dependent transformations Scaling and encoding fitted on training data, applied on read.

Read

Training data Splits by ratio or time, materialised or in memory.
Batch data Inference data for a time range, with transformations applied.
Feature vectors Single or batched online lookups by serving key.
Feature server Online lookups over REST, without the Python client.

Observe

Feature monitoring Compare new data against a training dataset.
Feature logging Log the features a model actually saw at inference.