Advantages of feature store – Vertex AI Feature Store

by Ron Mutschler Exams of Google, Feature attribution methods, Google Certification Exams, Limitations of Explainable AI

These are the advantages of feature store:

  • Extend features company-wide: Feature stores let you easily share features for training or serving. Different projects and use cases do not need feature re-engineering. Manage and deliver features from a central repository to preserve consistency throughout your business and prevent redundant efforts, especially for high-value features.

Vertex AI Feature Store lets people find and reuse features using search and filtering. View feature metadata to assess quality and usage. For instance, you may check feature coverage and feature value distribution.

  • Serving at scale: Online forecasts require low-latency feature serving, which Vertex AI Feature Store manages. Vertex AI Feature Store automatically builds and expands low-latency data serving infrastructure. You create features but outsource providing them. Data scientists may create new features without worrying about deployment using this management.
  • Reduce training-serving bias: Training-serving skew happens when your production feature data distribution differs from the one used to train your model. This skew causes disparities between a model’s training and production performance. Vertex AI Feature Store can handle training-serving bias with these examples:
    • Vertex AI Feature Store guarantees that feature values are ingested once and reused for training and serving. Without a feature store, training and serving features may use distinct code paths. Training and serving feature values may differ.
    • Vertex AI Feature Store offers previous data lookups for training. By collecting pre-prediction feature values, these lookups reduce data leakage.
  • Identify drift: Vertex AI Feature Store detects drift in feature data distribution. Vertex AI Feature Store monitors feature value dispersion. Retrain models using impacted features as feature drift rises.
  • Retention: The Vertex AI Feature Store preserves feature values for the allotted amount of time. This cap is determined by the feature values’ timestamp, not the date and time the values were imported. Values with timestamps that go beyond the limit are scheduled for deletion by Vertex AI Feature Store.

Disadvantages of feature store

A feature store has overhead, which can make data science more complicated, especially for smaller projects. A feature store may complicate matters if a business has numerous little data sets. Feature stores are ineffective when the data is so diverse that no standard modeling approach will assist. Reusing features created on separate data sources and metadata is tough. Additionally, a feature store might not be the ideal choice when the features are not time-dependent or when features are needed only for batch predictions.