The control plane that keeps your models honest after launch
Training pipelines, model registries, shadow deployment, statistical drift detection, evaluation on a schedule and lineage an auditor will accept. Built on AWS SageMaker, Azure ML or GCP Vertex AI, and equally applicable to classical models and LLMs.
A model in production without governance is a liability with a good demo
Models decay quietly. The data distribution moves, an upstream field changes meaning, a provider updates a model behind the same endpoint name, and accuracy slides for weeks before anyone notices. In a regulated environment, that is not a technical problem, it is an audit finding.
We build the layer that makes model behaviour observable and reversible: what version is live, what data it was trained on, who approved it, how it scored on the last evaluation and what the drift monitor has seen since.
The same discipline applies to LLM features. Prompt changes, model upgrades and retrieval tweaks all need versioning, evaluation and a way back, which is what separates LLMOps from hoping the vendor does not change anything.
Why these projects fail
These are the gaps we find when we audit an existing production setup.
How we put a control plane around your models
We can start from an existing production mess or from a clean slate. The first week is an honest audit either way.
Inventory and audit
Every model, endpoint, prompt and dataset currently in production, with owner, version, dependencies and known evaluation history. Most clients find at least one model nobody claims.
Registry and lineage
A single registry holding versions, training data references, hyperparameters, evaluation results and approval records, so any live version can be traced end to end.
Reproducible pipelines
Training and retraining as versioned pipelines rather than notebooks, with pinned dependencies and artefacts stored per run.
Automated evaluation
Scheduled evaluation against held-out sets and business metrics, with results published to the registry and alerts on regression.
Drift detection
Statistical monitoring of input and prediction distributions against rolling baselines, with thresholds tuned to your tolerance rather than a library default.
Shadow deployment and A/B
New versions run against live traffic without serving it, then take a controlled share once the numbers hold. Rollback is a configuration change.
Governance and reporting
Approval workflows, immutable audit records and reporting your compliance team can hand to an auditor without a translation exercise.
What a control plane over your models looks like
Shadow deployment is highlighted because it is the step that turns a risky promotion into a measured one. The drift monitor below is what catches the failure nobody reported.
What lands in your infrastructure
Infrastructure your ML engineers own, on the cloud you already run.
Tools we build this on
Chosen per project against your data, latency budget and compliance requirements, not out of habit.
MLOps Platforms
Monitoring and Evaluation
Infrastructure
Data Layer
Governance work already running in regulated environments
Numbers from live production systems, not projections.
Five production models under one governance layer
Scheduled evaluation, statistical drift detection, shadow deployment and a model registry with SOC 2 compliant lineage, replacing model tracking that lived in spreadsheets.
DevOps · USADelivery pipeline instrumented end to end
Automated pull request review, failure diagnosis and anomaly watching on deployments, cutting debugging and incident time 38% and shortening deployment cycles 29%.
Retail · USAReinforcement learning in production under 100ms
A personalisation layer that improves from live behavioural signals, with demand forecasting accuracy up 62%, running through AWS Bedrock with monitored retraining.
MLOps questions we answer on most first calls
What clients ask before starting a project like this.
Do you work with models we already have in production?
Usually, yes. Most MLOps engagements start with an audit of what is already live rather than a rebuild. We wrap governance around existing models first, then improve the pipelines behind them.
Is this only for large machine learning teams?
No. A team of two running three models benefits from a registry, scheduled evaluation and a rollback path more than a large team does, because there is no one on standby to catch a silent regression.
Does LLMOps need different tooling from classical MLOps?
It needs additional layers rather than different foundations. Prompt versioning, retrieval evaluation, token cost tracking and trace observability sit alongside the registry, drift monitoring and CI/CD you already need for classical models.
Can you make our setup audit-ready for SOC 2 or GDPR?
We build the lineage, approval workflow and immutable records auditors ask for, and we have delivered a SOC 2 ready governance layer in financial services. Certification itself is your auditor's decision, and we prepare the evidence they will request.
How long does an MLOps engagement take?
An audit with a prioritised remediation plan takes about two weeks. A full control plane across several models typically runs 8 to 14 weeks, delivered in stages so each layer is in use before the next begins.
How would you prove your live model still works?
If the answer involves opening a notebook or asking the person who built it, book a call. We will walk through what a control plane over your current setup would look like.