Operational practices for AI systems that stay reliable over time.
Overview
The operational side of AI at scale: model versioning, deployment automation, drift detection, and lifecycle management. We build the tooling and practices that let teams confidently ship AI systems and keep them running.
Research Directions
Continuous training pipelines
Automated retraining triggered by distribution drift signals, not calendar schedules.
Statistical drift detection
Population stability index and embedding drift metrics for early warning on input distribution shifts.
Experiment tracking at scale
Versioned experiment graphs linking datasets, hyperparameters, and evaluation results.
Model cards automation
Auto-generated model documentation from training metadata and eval outputs.
Canary deployments for LLMs
Shadow scoring and progressive traffic shifting with automatic rollback on quality regression.
Traditional software deployments fail deterministically: a bug either crashes the process or it does not. AI systems fail gradually and silently. A model can degrade in quality over weeks as its input distribution drifts from the training distribution, with no error logs and no alert. Detecting and responding to this class of failure requires a different operational mindset.
We monitor three layers: input drift (are incoming queries changing?), output drift (are model responses changing?), and outcome drift (are downstream task success rates changing?). Each layer requires different instrumentation. Input and output drift are detectable with embedding distance metrics. Outcome drift requires labeled feedback loops, which we integrate via implicit signals (user corrections, thumbs-down events).
Population Stability Index
PSI measures how much the distribution of a feature (e.g., query embedding PCA projection) has shifted relative to a reference window. PSI below 0.1 is stable; above 0.25 triggers a retraining alert.
def psi(expected: np.ndarray, actual: np.ndarray, buckets: int = 10) -> float:
"""Population Stability Index. PSI > 0.25 indicates significant drift."""
expected_pct = np.histogram(expected, bins=buckets)[0] / len(expected)
actual_pct = np.histogram(actual, bins=buckets)[0] / len(actual)
# avoid log(0)
expected_pct = np.clip(expected_pct, 1e-6, None)
actual_pct = np.clip(actual_pct, 1e-6, None)
return float(np.sum((actual_pct - expected_pct) * np.log(actual_pct / expected_pct)))We route 5% of production traffic to the candidate model and compare quality scores from a frozen judge model against the current production baseline. If the candidate scores within a 2% margin for 30 minutes, traffic shifts to 25%, then 100%. Any regression triggers automatic rollback within 90 seconds. This approach has shipped 23 consecutive model updates without a user-visible quality incident.
Every production model artifact is registered with a full lineage record: source dataset hashes, training run ID, hyperparameters, evaluation results, and the deployment history. This makes incident investigation deterministic: given a timestamp, we can reconstruct exactly which model version was serving, what it was trained on, and what its pre-deployment eval scores were.