Feature Store Setup
Feature engineering is 80% of ML work, and most teams recompute the same features across training and serving — introducing skew. We set up a feature store that serves consistent features to both training pipelines and real-time inference, eliminating the #1 source of production ML bugs.
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Offline & Online Stores
The offline store (BigQuery, Redshift, or Parquet on S3) handles batch feature retrieval for training. The online store (Redis, DynamoDB, or PostgreSQL) serves low-latency lookups for real-time inference. We configure materialization jobs that keep the online store in sync with the offline source — typically running on a schedule via Airflow or as streaming jobs from Kafka.
Feature Definitions & Versioning
Features get defined as code — version-controlled, reviewed, and tested like any other infrastructure. Feature views map raw data to computed features with explicit schemas and entity keys. When a feature definition changes, both historical and real-time paths update consistently. No more 'the training pipeline computes this differently than the API'.
Pipeline Integration
Training pipelines pull point-in-time correct feature vectors from the offline store. Inference services query the online store with sub-10ms latency. We integrate the feature store with your existing ML pipeline (Kubeflow, Airflow, or custom) so feature retrieval is a single function call, not a bespoke data pipeline per model.
Monitoring & Data Quality
Feature freshness monitoring alerts when materialization jobs fall behind. Distribution drift detection catches upstream data changes before they corrupt model inputs. Schema validation rejects malformed feature values at write time. You get dashboards tracking feature serving latency, cache hit rates, and staleness across all feature views.
Why Anubiz Engineering
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Skip the research. Tell us what you need, and we'll scope it, implement it, and hand it back — fully documented and production-ready.