Failure Modes in Continuous Training Pipelines

Production continuous training systems fail in predictable ways. Training serving skew is the most common: feature definitions diverge between offline and online code paths. Airbnb discovered a ranking model performed 15 percent worse in production because an online aggregation window was 60 minutes instead of the 24 hour window used in training. The fix is a single declarative feature definition in a feature store with automated parity validation, monitoring L2 distance between offline and online features and alerting when it exceeds thresholds (typically 0.01 for normalized features). Feedback loops create self reinforcing bias. Netflix recommendation models that show popular content make it more popular, skewing training data and reducing diversity. This causes filter bubbles and long term engagement drops. Mitigate with exploration mechanisms (epsilon greedy with 5 to 10 percent random recommendations, Thompson sampling for bandit problems), propensity score reweighting to debias training data, and counterfactual evaluation using historical logs. Uber fraud models face adversarial feedback: blocking one attack vector causes fraudsters to shift tactics, creating concept drift that looks like model improvement in offline metrics but is actually an arms race. Label delay and leakage are subtle but catastrophic. Meta ad models predict conversions that occur 1 to 7 days after click, but training on all available labels at snapshot time includes future information. The solution is strict event time semantics: only use labels available at serving time, implement watermarking to handle late arriving labels, and validate with time based splits (train on week 1, validate on week 2) rather than random splits. Data outages and schema drift silently corrupt features: add schema contracts with type checking and range validation, canary new features on 1 percent of training data before full rollout, and abort training jobs when validation metrics (null rate, distribution stats) exceed thresholds.

Uber fraud detection faced adversarial feedback loops where blocking one attack vector caused fraudsters to shift tactics, requiring adversarial training with synthetic attack patterns and robust loss functions to handle distribution shift

Meta discovered a conversion prediction model leaked future information by including conversions that occurred after the prediction timestamp, causing 25 percent accuracy drop in production fixed by event time windowing with 7 day label delay handling