AI/ML PRODUCTION

Feature Engineering

Stop rewriting transformations for every model. Centralize your logic, eliminate training-serving skew, and accelerate model development with enterprise-grade Feature Stores.

Centralize Your Features

The Power of the Feature Store

Data scientists spend 80% of their time engineering features and only 20% training models. Even worse, the translation of these offline features to real-time online inference pipelines often leads to discrepancies and prediction errors.

Numstack implements robust Feature Stores (like Feast or AWS SageMaker Feature Store) that act as a single source of truth for your ML platforms.

  • Avoid Training-Serving Skew
  • Millisecond Online Latency
  • Scalable Offline Training Generation
@feature_view(
    name="user_transaction_aggregates",
    entities=[user],
    ttl=timedelta(days=30),
    schema=[
        Field(name="daily_spend", dtype=Float32),
        Field(name="category_pref", dtype=String),
    ],
    online=True,
    source=spark_batch_source
)

# Features retrieved deterministically in ~5ms globally.

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