MLOps
While the world discusses agents, your fraud scores, demand forecasts, and recommendation models keep doing quantifiable work — or quietly rotting. Classical ML doesn't announce its failures in chat; it drifts, silently, as the world walks away from the training data. Operating these models well is a solved discipline that most organizations still haven't implemented.
We build the machinery: training pipelines that are reproducible instead of tribal, registries that know which model version is serving and why, feature stores that end training/serving skew, drift monitoring that catches distribution shifts before the business does, and retraining loops with human sign-off where the stakes demand it. All of it on the Kubernetes-grade foundations we run everywhere else — GPU-scheduled, autoscaled, observable.
Training pipelines
Reproducible, versioned training from data snapshot to registered artifact — no more "it worked on Dave's laptop."
Model registry and lineage
Every serving model traceable to its data, code, and metrics — audit answered by design.
Feature engineering infrastructure
Feature stores and pipelines that kill training/serving skew.
Drift detection
Input and prediction monitoring that pages before accuracy decays into losses.
Serving on Kubernetes
Autoscaled, GPU-efficient inference with canary rollouts for new model versions.
Is classical MLOps still relevant in the LLM era?
Completely. Fraud, forecasting, pricing, and ranking still run on trained models with measurable ROI, and they fail in ways LLM tooling doesn't address — silent drift chief among them. The organizations doing agents well are usually the ones that got this discipline right first.
Our data scientists hand models to engineers by email. How bad?
Common, and expensive: weeks of deployment friction per model and zero reproducibility when something breaks. A registry, packaging standards, and an automated path from experiment to serving typically cut model-to-production time from months to days.
How do we know when to retrain?
From monitoring, not calendars. Drift metrics on inputs and outcomes tell you when the world has moved; scheduled retraining without them either wastes compute or misses the shift. We wire the signals first, then automate the response.
Put MLOps to work — in production.
One forward-deployed engineer, embedded in your stack, owning the outcome from discovery to production. Weeks, not quarters.
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