MLOps & AI DevOps
Software that breaks throws errors. A model that breaks keeps returning confident predictions that are increasingly wrong, and nothing in your monitoring notices. That asymmetry is why AI systems need operational practice that traditional application monitoring does not provide.
Silent decay
Three things go wrong after launch and none of them page anybody. The input distribution moves — a new customer segment, a changed upstream form, a supplier who reformats their file — and the model is now extrapolating rather than interpolating. The relationship itself changes, so yesterday's correct model is today's wrong one even though the inputs look normal. And the model cannot be reproduced: the training data was a query against a table that has since been updated, the exact library versions are gone, and nobody can rebuild the artefact that is currently making decisions. That last one is discovered during an audit or an incident, which is the worst possible moment.
The three calls that decide the outcome.
Made explicitly, with the trade-off written down, before anything gets built.
Which kind of drift you are actually monitoring
Data drift is cheap to detect and frequently harmless. Concept drift is what hurts, and inputs can look entirely stable while the relationship underneath them has moved. Performance monitoring catches it directly but requires labels, which often arrive weeks later or never. The practical design uses input monitoring as an early warning, prediction-distribution monitoring as a proxy where labels lag, and true performance measurement wherever ground truth eventually lands.
Scheduled retraining or triggered
Retraining on a fixed schedule is simple and either wasteful or too slow, usually both at different times of year. Triggered retraining responds to measured degradation but needs a reliable signal and a guard against retraining on the model's own feedback loop. Most production systems end up with both: a scheduled floor so nothing goes stale unnoticed, plus triggers on drift and performance thresholds. What matters more than the choice is that a retrained model cannot reach production without passing the same evaluation gate as the original.
How a new model reaches production
Replacing a live model in one step means discovering problems on all of your traffic at once. Shadow deployment runs the candidate alongside production without acting on its output, which catches serving skew and latency problems safely. Canary release exposes a small share of traffic with an automatic rollback trigger. Both require the old model to remain deployable, which in turn requires the reproducibility that most teams discover they do not have.
The capabilities we bring on day one.
Each engagement assembles from this menu, sized to your scope, paced to your calendar.
Drift detection that matters
Input, prediction-distribution and true performance monitoring layered so lagging labels do not blind you.
Reproducible artefacts
Versioned data, code and models, so any deployed model can be rebuilt and any decision explained.
Safe rollout
Shadow and canary patterns with automatic rollback, so a bad model never meets all your traffic at once.
Retraining with a gate
Automated pipelines where a retrained model still has to pass the same evaluation bar as the original.
Lineage and post-market monitoring
For systems in the EU AI Act's high-risk tier, whose obligations start applying on 2 August 2026, ongoing monitoring after deployment is an explicit requirement rather than an engineering nicety — as is the ability to reconstruct which model version made a given decision, on what data, under what configuration. In practice this is the same capability good MLOps produces anyway: versioned data, versioned artefacts, immutable prediction logs and a lineage record connecting them. Teams that build it for regulatory reasons usually find it is what they needed for debugging regardless.
From kickoff to live in four phases.
Each phase has named deliverables, named owners and a named gate, and every one respects the systems you already have in place.
Discover
Inventory of models in production, who owns each, what is monitored today, and which ones could not be rebuilt if asked.
Design
Monitoring strategy per model against label availability, retraining policy, rollout pattern and the lineage record needed for audit.
Build
Pipelines, feature reuse between training and serving, evaluation gates in CI, and monitoring wired to the alerts your team already answers.
Run & hand off
Runbooks for drift and rollback, on-call enablement, and handover so retraining is a routine operation rather than a project.
What a good fit looks like.
Stated up front, so neither of us spends a call finding out this was the wrong conversation.
Engagement shape
An embedded pod, usually monthly, since operational practice is proven by running it — often starting with the one model whose failure would hurt most.
From your side
Access to production models and their serving infrastructure, the teams that own them, and whatever ground truth eventually becomes available.
When not to hire us
If you have no models in production yet, this is premature. Build the first one with these practices included rather than treating MLOps as a later retrofit.
MLOps & AI DevOps questions, answered.
By layering the signals. Input monitoring gives early warning and is available immediately. Prediction-distribution monitoring catches a shifting output profile without needing ground truth. True performance measurement lands whenever labels do and confirms or corrects the earlier signals. Relying on performance alone means finding out about degradation a reporting cycle late.
Most production systems want both a scheduled floor so nothing goes stale unnoticed and triggers on measured drift or performance loss. The frequency matters less than the gate: a retrained model should have to pass the same evaluation bar as the original before it can reach production, otherwise automated retraining just automates the decay.
Yes. MLflow, Kubeflow, SageMaker, Vertex, Databricks and plain containers on Kubernetes all work. The practices matter more than the tooling, and replacing a platform your team already knows is rarely worth what it costs in adoption.
That is common and it is the right thing to fix first, because reproducibility is what every other practice depends on — you cannot roll back to a model you cannot rebuild. Start by versioning data snapshots and training artefacts going forward, then reconstruct backwards for the models whose failure would hurt most. Perfect historical reconstruction is often not achievable; a clean line from today usually is.