Deploying a model once is a project. Keeping it reliable in production is a discipline. We build the infrastructure that turns AI experiments into production systems with version control, automated testing, monitoring, and continuous delivery.
Discuss your MLOps needsThe world changes. Customer behaviour shifts. Data distributions drift. New edge cases appear. A model deployed in January is making worse predictions by April, and nobody notices until a business metric drops. Without MLOps, your AI system is a snapshot that degrades silently. With MLOps, it's a living system that monitors itself, flags problems, and improves over time.
Every training run recorded. Every hyperparameter logged. Every model version stored with its training data, evaluation metrics, and lineage. When something goes wrong in production, you can trace back to exactly what changed and when. When you want to compare approaches, all experiments are side by side.
Scheduled retraining on fresh data with validation gates at every step. New data arrives, the pipeline validates it, retrains the model, evaluates against the current production model, and promotes only if performance improves. No manual intervention, no forgotten retraining schedules.
A/B testing between model versions. Canary releases that route 5% of traffic to the new model before full rollout. Shadow mode where the new model runs alongside production without serving results. Instant rollback if anything degrades. Your model deployment has the same rigor as your software deployment.
Real-time dashboards showing prediction distributions, latency, error rates, and feature drift. Automated alerts when data drift exceeds thresholds. Automated retraining triggers when model performance degrades below targets. You know the health of your AI system at all times. Not when a customer complains.
We don't do web apps on the side. Every engineer on your project has deep AI specialisation and has deployed production ML systems before.
We don't implement the first architecture that works. We explore options, test assumptions, and design the solution that fits your specific constraints.
Our AI-augmented methodology compresses delivery timelines by 2-3x compared to traditional consulting.
Timeline
6-10 weeks
Team
2 senior MLOps engineers
Deliverables
CI/CD pipeline for models, experiment tracking infrastructure, model registry, monitoring dashboards, alerting system, drift detection, runbook documentation
After launch
Optional retainer for pipeline maintenance and optimisation as you add new models
Research shows that ML models in production degrade measurably within 3-6 months without active monitoring and retraining. A model that was 95% accurate at launch might drop to 80% within a quarter as the underlying data distribution shifts. Most teams don't detect this until a downstream business metric moves. By which point the damage is done and the fix is urgent. MLOps turns reactive firefighting into proactive maintenance. The monitoring catches drift before it affects business outcomes. The automated retraining keeps the model current. The versioning gives you rollback when something unexpected happens. The cost of MLOps is a fraction of the cost of a production model failure.
Software CI/CD and ML CI/CD share the concept but differ in execution. Software tests are deterministic. The same input always produces the same output. ML tests are statistical. You're measuring accuracy distributions, not pass/fail. We integrate with your existing CI/CD tooling (Jenkins, GitLab CI, GitHub Actions) and extend it with ML-specific stages: data validation, model evaluation, performance regression testing, and drift detection.
We work with MLflow, Weights & Biases, Kubeflow, Vertex AI, SageMaker, or custom tooling depending on your existing infrastructure. We'll recommend what fits your setup, not force you onto a specific platform.
Virtually none. Monitoring is asynchronous. Prediction requests are served at full speed, and monitoring data is logged in the background. Dashboard updates happen on a schedule (typically every few minutes), not per-request. The only latency impact is the logging itself, which adds sub-millisecond overhead.