Aurum Quanta MLOps.
The infrastructure that keeps models working on Tuesdays.
The infrastructure that decides whether your models survive contact with a Tuesday morning. Drift detection, automated retraining, A/B testing, model registries, rollback procedures. The unglamorous parts most projects skip in week one and end up writing a postmortem about in month nine.
We harden existing models for production, or design the platform before the first model is trained, depending on what stage you're already at.
Drop in predictions. See how a model is actually evaluated.
Binary · 500 scored samples · balanced · 2 classes
Pick a scenario or paste your own predictions. The calculator builds the confusion matrix, derives per-class precision, recall, F1, and surfaces the gap between macro-F1 and weighted-F1 - the gap that exposes the accuracy paradox on imbalanced data. Try the fraud preset: 99% accuracy looks great until you read the recall column.
Note · this is a simplified demo
A real engagement would wire this same evaluation discipline into a production pipeline. Eval gates would block deploys when macro-F1 drops below threshold; shadow traffic would compare the candidate against the live model before any switch; data-drift detectors would watch input distributions and trigger retraining; bias audits would slice metrics by cohort. The numbers you see here are a snapshot. Production MLOps is the discipline of making sure they stay honest, week after week.
Train, ship, monitor, retrain.
A model in production is never finished. Every deployment cycles through evaluation, staging, rollout, and monitoring - drift detection feeds the next training run. The work isn't building a model; it's keeping one alive. The cycle is the work.
Concrete deliverables.
Drift detection and monitoring
Input drift, output drift, and performance regressions, alerted to the people on call before the customer-facing failure registers downstream. The cheap version of this you can stand up in an afternoon catches most of what matters.
Automated retraining
Scheduled, triggered, or on-demand retraining pipelines with test harnesses, shadow deploys, and a rollback path that doesn't require a redeploy.
Model registry and rollback
Versioned models with reproducible training runs. A bad deploy gets reverted with one command, which is what makes the deploy itself safe to do in the first place.
Audit-ready logging
Every prediction, feature value, and model version is traceable for as long as your retention policy demands (usually seven years in Australian financial services). Designed for regulated environments from day one, because retrofitting it is a project of its own.
Statistical drift detection. Pages on-call before users notice.
# monitors/drift.py: detect distribution shift in production
def check_drift(reference: np.ndarray, current: np.ndarray, alpha: float = 0.01) -> None:
statistic, p_value = ks_2samp(reference, current)
if p_value < alpha:
alert(
channel="#ml-oncall",
message=f"Drift detected: KS={statistic:.3f}, p={p_value:.4f}",
runbook="wiki/runbooks/drift",
)Two-sample Kolmogorov–Smirnov test. Alerts include a runbook link, not just a metric.
How it would unfold.
Audit
Current state review of models, pipelines, and gaps. Prioritised remediation plan.
Pilot
One model taken end-to-end: retraining, monitoring, registry, rollback, alerting.
Rollout
Platform pattern extended to remaining critical models, with team trained on operation.
Ongoing
SRE-style on-call support for model incidents, tuning, and platform evolution.
Tools we reach for on this kind of work.
Common questions.
If we already have MLflow and Airflow, do we need this?
Often the tools are there and the discipline around them isn't. We work inside whatever stack you've already paid for and focus on the process and ownership. Re-platforming you onto something we'd prefer isn't part of the engagement.
Can you harden an existing broken pipeline?
Yes, a lot of our MLOps work is exactly this. The first move is usually to stop the bleeding (rollback, a heuristic safety net, manual review on the worst-affected segment) and the second is to build the missing infrastructure around the model so it doesn't break the same way again.
How is this different from DevOps?
ML systems have failure modes DevOps tooling doesn't cover well: data drift, concept drift, silent accuracy decay, feedback loops that train the model on its own outputs. MLOps is essentially DevOps with monitoring for those.
Let's build it.
A 30-minute discovery call. We'll tell you whether we're the right shop for this.
Book a discovery call →