AI Data Pipeline Template for AI Products

Use the feature freshness requirement as your guide. If the model needs data updated in under 5 minutes, you need a streaming pipeline. If daily or hourly updates are sufficient, batch is simpler, cheaper, and easier to debug. Most AI products use a hybrid: real-time features (session data, current context) via streaming, and historical aggregates (30-day purchase count, lifetime value) via batch.

Feature drift means the statistical distribution of your input features has changed compared to what the model was trained on. Detect it by computing Population Stability Index (PSI) or Kolmogorov-Smirnov tests daily against your training data distribution. A PSI above 0.2 warrants investigation. Common causes: upstream schema changes, seasonality, new user segments, or data collection bugs.

It depends on the model type. For fine-tuning LLMs, 500-2,000 high-quality labeled examples can produce meaningful improvements. For traditional ML classifiers, 10,000-100,000 labeled examples is a reasonable starting point. For recommendation systems, you typically need 3-6 months of behavioral data. Start with less data, validate the approach works, then invest in scaling data collection.

For teams with fewer than 5 ML engineers and fewer than 50 features, a simple solution (Redis for online, S3/Parquet for offline) works fine. Managed feature stores (Feast, Tecton, Hopsworks) become worthwhile when you have 50+ features, multiple models sharing features, or strict freshness SLAs that require automated materialization. The operational overhead of running a feature store is real. Do not adopt one prematurely.

Build a feedback loop. Log model predictions alongside the features used. When users report bad predictions, trace back to the input features. Implement automated anomaly detection on feature values at serving time (not just in the batch pipeline). Add circuit breakers that fall back to default predictions when feature quality drops below thresholds.