MLOps Engineering
The machinery that ships and updates production models: versioned data, a registry you can trust, signed artefacts and retraining that passes the same gate as the first release.
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Tell us the pipeline
Teams come to us when shipping a model has become slower or riskier than building it: nobody can say which data trained the model in production, a registry exists but deployment ignores it, or retraining is a manual project every time. In regulated settings the same gaps surface as audit findings.
MLOps here means the delivery machinery. Detecting drift and degradation in production is covered under Production AI Reliability, and the evidence an approver reads under AI Governance and Trust.
Where We Help
Lineage, Gated Deployment and Retraining
Scoped to the pipeline you have, not a platform rewrite.
Lineage and Registry
Trust
Versioned data, tracked experiments and a registry linking every artefact to its run.
Gated Deployment
Trust
CI/CD that deploys only signed, registered artefacts, with no manual override path.
Retraining Pipelines
Reliability
Retraining on defensible triggers, through the same evaluation gate as the first release.
How It Connects
Monitoring and tracing of the deployed model land in the Production AI Monitoring Harness.
Tell us about the pipeline and where it slows you down.
MLOps Engineering FAQ
Can we prove which training run and dataset produced the model in production?
Yes, if lineage is recorded when the model is built rather than reconstructed later. We set up dataset versioning, experiment tracking and a registry that links each artefact to its run and data, with a hash or signed manifest that the deployment checks.
How do we stop an unregistered model reaching production?
By making the pipeline refuse it. We wire the CI/CD so it deploys only artefacts whose hash matches a signed registry entry, with no manual override path.
When should a model retrain, and who approves the result?
Retraining should follow a trigger you can defend (drift, new data or a schedule) and pass the same evaluation and approval gate as the first release. We build that pipeline, so retraining is routine rather than a project.