pipelines

Hand-tuned prompts break on the next model upgrade and don't compose across pipeline stages. Frameworks like DSPy compile prompts against a metric instead. The signal is treating prompting as an optimized program. Here is the answer.

Pipelines fail and retry; the real question is whether a retry corrupts your data. The signal is idempotent writes (not 'exactly-once delivery') plus watermarks for late data. Here is how to make re-runs safe without double-counting.

Bad data silently poisons everything downstream (dashboards, models, decisions), and 'it ran without error' is not 'it's correct.' The signal is the categories of checks and failing loud at the boundary.

A new column is safe; a rename silently corrupts every dashboard downstream. The signal is knowing which changes are compatible, enforcing data contracts in CI, and running expand-contract migrations for the breaking ones.

A backfill that double-counts or takes down the live pipeline is worse than the original bug. The signal is idempotent partition-scoped rewrites, isolating backfill compute from live runs, and validating before swapping.

The naive answer is 'retrain weekly.' The senior answer is a hybrid: drift-triggered retraining with a max-staleness fallback, plus a gate that a fresh model must beat the incumbent. Here is how to reason about it.

A feature store is easy to define and easy to get wrong. The signal is explaining the exact bug it prevents (training-serving skew) and the point-in-time correctness that stops label leakage. Here is the full picture.

ML workflows are multi-step DAGs (ingest, feature, train, eval, deploy), and orchestrators run them reliably. The signal is the DAG/scheduling model plus what's actually ML-specific: data deps, versioned artifacts, drift triggers, eval gates.