ML infrastructure that runs itself.
The infrastructure that keeps AI running after launch day. Automated retraining, model versioning, drift detection, and monitoring that pages when it matters. Not a notebook in production.
Model serving uptime
From code merge to model deploy
Faster model iteration cycles
Silent model degradations in production
What we build
MLOps for production AI.
CI/CD for ML models
Automated training, validation, and deployment pipelines. Code changes trigger retraining, evaluation gates catch regressions, and promotions are one-click.
Model versioning & registry
Every model version tracked with its training data, hyperparameters, and metrics. Rollback to any version instantly. Full reproducibility.
Drift detection & alerts
Data drift, concept drift, prediction drift — we detect them all. Automated alerts before your model silently degrades in production.
Auto-retraining pipelines
Models that improve automatically as new data arrives. Scheduled or triggered retraining with evaluation gates to prevent regressions.
Production monitoring
Latency, throughput, error rates, prediction distributions. Custom dashboards that show model health, not just infrastructure metrics.
A/B testing & shadow mode
Test new models against production baselines with real traffic. Shadow mode for zero-risk evaluation, gradual rollouts for controlled deployments.
Sound familiar?
MLOps problems we solve every month.
“Our data scientist deploys models from Jupyter notebooks via SSH. It takes days.”
We build automated CI/CD pipelines. Push code, run training, pass evaluation gates, deploy. From notebook to production API in under 15 minutes.
“Our model accuracy dropped 20% and nobody noticed for 3 weeks.”
We implement drift detection and prediction monitoring. You get alerts the moment model performance degrades — not when customers complain.
“We can't reproduce our best model because we lost track of the training parameters.”
We set up experiment tracking and model registry. Every run logged with data version, hyperparameters, metrics, and artifacts. Full reproducibility.
Common questions
MLOps & Model Serving, answered.
What does MLOps cover that DevOps does not?
Everything that follows from a model being a moving target. Code behaves the same on Tuesday as it did on Monday. A model degrades as the world it was trained on shifts, so you need versioned datasets, reproducible training, monitoring for drift, and a rollback path for a model and not only for a deployment.
What is model drift and how do you detect it?
Drift is the gap that opens between the data a model was trained on and the data it now sees. It shows up first as a change in the input distribution and later as a drop in accuracy, usually noticed by a customer. Detection means monitoring both: the shape of incoming data, and performance against labelled examples you keep current.
How often should a model be retrained?
On evidence rather than on a calendar. Retraining monthly because it feels prudent burns money and can make things worse. Retraining when a monitored metric crosses a threshold you set in advance ties the cost to an actual need. That requires the metric to exist first, which is where most teams are stuck.
Do we need a feature store?
Only when training and serving genuinely disagree, or when several teams reuse the same features. For a single team with one model, a feature store adds infrastructure to operate for a problem you may not have yet. The failure it prevents, training and serving skew, can also be prevented by sharing one transformation path.
Can MLOps tooling tell us whether the model is accurate?
No, and this is the most common misunderstanding. Pipelines, registries and dashboards tell you what ran, when, and at what cost. Accuracy requires a labelled reference to compare against. We build that layer separately, as a fixed-scope reliability assessment, because monitoring infrastructure cannot produce it.
Tech stack
Tools we use in production.
Ready to build
Let's build MLOps that scales.
45 minutes with our MLOps engineers. We'll audit your current ML workflow, identify bottlenecks, and design the infrastructure to ship models faster and safer.
AI projects we delivered
