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.
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
