A model that performs well offline and degrades silently in production is a liability. We build the infrastructure that catches it.
Most ML projects fail after launch, not before it. A model validated on a static test set doesn't stay accurate as the world it describes keeps changing.
Production reliability is an infrastructure problem as much as a modelling one: feature pipelines that compute training and inference features identically, so there's no silent train/serve skew; a serving layer that meets the latency and throughput the application actually needs; and monitoring that catches data drift, prediction drift, and label drift before they show up as a business problem.
We design the retraining trigger — scheduled, drift-based, or performance-based — to match how fast your data actually moves, and build the evaluation gate that stops a retrained model from shipping if it's worse than what's already live.
The goal is a model that keeps working six months after handoff, not just on the day it's deployed.
How we approach it
Defining latency, throughput, and availability requirements against the actual application, then choosing batch, online, or streaming serving accordingly — not defaulting to the most complex option.
Building feature computation so training and inference use identical logic — the single most common cause of production models that quietly underperform their offline evaluation.
Instrumenting input distributions, prediction distributions, and — where ground truth arrives with a delay — realised performance. Alert thresholds tuned to avoid both alert fatigue and missed degradation.
Defining when retraining happens — on a schedule, on detected drift, or on a performance floor — and an automated evaluation gate that blocks a new model version from replacing a better-performing one already in production.
Runbooks for on-call response to model alerts, full pipeline documentation, and a rollback path — so the system stays operable by your team long after the engagement ends.
What we build
Feature pipeline to monitored serving
Drift detected at serving triggers an alert — and, past a defined threshold, a retraining run.
Tell us how it's behaving and we'll scope it together — no obligation.
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