A model that performs well in the training and test environment but that degrades silently in production doesn't help anyone. We build the monitors that catch those kinds of behaviours.
A model validated on a static test set is not guaranteed to stay accurate as the world it describes keeps changing. How many samples of new products or classes do you need to update an existing model and ensure it adapts properly? We do the work to find out.
Production reliability is an infrastructure problem as much as a modelling one: feature pipelines that compute training and inference features predictably and repeatably, 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 any drift of data, predictions, or labels so that behaviour is understood.
We design for various retraining triggers, whether they be scheduled, drift-based, or performance-based. This allows the system to closer match how fast your data actually moves. We also create the evaluation gate that stops a retrained model from shipping if it's worse than what's already live.
The goal is a pipeline that keeps producing working models six months after handoff, not just the first one.
How we approach it
Defining existing and reusable architectures, latency, throughput, and availability requirements against the actual application, then choosing batch, online, or streaming serving accordingly.
Building feature computation so training and inference use the same logic and the same inputs. Seems obvious, can be overlooked or subtly different without due consideration.
Instrumentation set up to gauge production distributions of inputs, predictions, and realised performance. Alert thresholds tuned to avoid both alert fatigue and missed degradation. Finding that balance between alert false positives and false negatives.
Defining when retraining or model/dataset updates happen — 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 allowing the system to stay 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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