MLOps for repeatable model delivery and operations.
CirOps builds and operates the MLOps practice on top of your AI infrastructure: training pipelines, model registry and versioning, deployment automation, and drift monitoring, run as a continuous operational discipline.
Engagements can be scoped as build-only or build-plus-operate, with ownership and approval boundaries documented before implementation.
The Operational Gap
Model delivery needs a repeatable production process
A model that performs well in evaluation still needs a reproducible pipeline to move the next version into production safely, a registry that tracks which model is actually serving traffic, or monitoring that catches drift before it shows up as a business problem.
Without that operational layer, model deployments rely on manual, inconsistent steps and performance changes may go unreviewed until the downstream impact is investigated.
What We Deliver
The operational layer between training and production
Training Pipeline Design
Reproducible, versioned training pipelines that replace ad hoc notebook runs, so a training result can be traced back to the exact data, code, and parameters that produced it.
Model Registry and Versioning
A defined system of record for every model: version, training lineage, evaluation metrics, and approval status, so deploying a model is a controlled decision, not a file copy.
Deployment Automation
CI/CD for models: automated testing, staged rollout, and rollback, applied with the same rigor as application deployment pipelines.
Feature Store Design
Consistent feature definitions shared between training and inference, closing the gap between what a model was trained on and what it sees in production.
Model Monitoring and Drift Detection
Production model performance is tracked against client-approved thresholds, with alerting when input distribution or prediction quality requires review.
Retraining and Experiment Operations
Defined retraining triggers and a structured experiment tracking practice, so model improvement is a continuous operational process, not a one-time project.
Is This Right For You?
Built for teams past the notebook stage
- →Data science teams with models that work in a notebook but no reproducible path to get a new model version into production.
- →Companies whose model performance has degraded in production and no one has visibility into when or why, because there is no drift monitoring in place.
- →Teams manually redeploying models through ad hoc scripts, with no version history, rollback path, or approval process.
- →Organizations that have the underlying AI infrastructure in place and need the operational practice layered on top of it.
Process
How we build and operate MLOps
MLOps Maturity Assessment
We document your current model lifecycle: how models are trained, versioned, deployed, and monitored today, and identify the specific gaps creating risk or slowing iteration.
Pipeline and Registry Build
We build the training pipeline, model registry, and deployment automation for your environment, integrated with the infrastructure you already run on.
Production Deployment
Models move through the pipeline into production behind defined evaluation gates and a staged rollout, with monitoring active from the first deployment.
Build or Operate
The engagement can end with documented handover or continue as build-plus-operate. Any ongoing monitoring, drift review, and retraining workflow follows the agreed ownership and approval model.
Trust & Credentials
MLOps operated with the same discipline as production infrastructure
AWS Advanced Tier Partner · Google Cloud Select Service Partner
Versioned
every model tracked from training to production
Monitored
drift detection active from first deployment
Reproducible
training pipelines, not one-off notebook runs
Common questions
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Ready to make model deployment a repeatable process instead of a manual event?
Request a no-cost Cloud Architecture Review. We assess your current ML workflow and give you a clear picture of what a production-grade MLOps practice would look like for your team.