Feature Store Versioning for Reliable ML Training Runs

Feature Store Versioning for Reliable ML Training Runs

Training inconsistencies often come from untracked feature changes. A versioned feature store makes experiments reproducible across weeks and teams.

Step 1: Version feature definitions as immutable specs

FEATURE_SET_V3 = {
    "user_age_days": "int",
    "sessions_7d": "float",
    "avg_watch_time": "float",
}

Step 2: Materialize with feature-set ID in output path

output = f"s3://ml/features/v3/date={run_date}/part-000.parquet"

Step 3: Persist training metadata with feature version

run_meta = {
    "model": "retention_xgb",
    "feature_set": "v3",
    "data_cut": run_date,
}

Pitfall

Reusing the same feature path while mutating transformation logic. You cannot explain metric changes later.

Verification

• Model card includes exact feature set ID.
• Older runs can be replayed with same feature snapshot.
• Feature schema drift triggers alert before training.