Services / AI Deployment and Operations
We take AI and ML models from pilot to production with the serving infrastructure, drift monitoring, rollback procedures, and operator-facing interfaces that make them actually used.
The problem
The model performs well in testing. The executive team approves the pilot. Then it sits in a Jupyter notebook for eight months because nobody has built the infrastructure to serve it at scale or monitor it in production.
Production AI systems fail in ways pilots don't. Data drifts. Upstream schemas change. Edge cases appear that weren't in the training set. Without monitoring and rollback procedures, nobody trusts the output.
We build the MLOps infrastructure, serving layer, and operator-facing interfaces that make models actually run in production and stay trusted by the people who use them.
Model training pipelines, experiment tracking, versioning, and CI/CD for model artifacts. You get reproducible training runs and a clear path from experiment to release.
Low-latency inference APIs, auto-scaling serving clusters, and canary deployment pipelines. Models served reliably under production load with rollback on degraded performance.
Data drift, model drift, and prediction distribution monitoring configured from launch. Alerting to your on-call team when model performance degrades before it impacts operations.
Dashboards and decision-support interfaces that surface model outputs to clinicians, field managers, and operations teams in a form they can act on. The model is only as useful as its interface.
Healthcare
Risk stratification, readmission prediction, and clinical pathway models deployed into workflows that clinicians can trust. HIPAA-compliant serving infrastructure with explainability built in.
Agriculture
Crop yield prediction, irrigation scheduling, and equipment routing models served to field operations teams in interfaces designed for use outside the office on variable connectivity.
Manufacturing
Anomaly detection and failure prediction models connected to historian and SCADA data, with operator dashboards that flag equipment problems before they become unplanned downtime.
One facility reduced unplanned downtime by 60% after deploying predictive maintenance to production.
We review your existing models, data pipelines, and serving requirements. You get a production architecture document covering inference design, monitoring strategy, and rollout plan.
MLOps pipelines, inference APIs, and drift monitoring built to the agreed architecture. Monitoring and alerting configured before the first model goes live.
Canary deployment, performance validation, and rollback testing. Runbooks and operator training delivered to the people who will run the system.
We stay available for the first 90 days. Model drift incidents, upstream data changes, scaling events. Your team owns it but we are there if something unexpected happens.
Related service
AI deployment requires a reliable data foundation underneath it.
Data Infrastructure and Engineering covers EHR integrations, IoT pipelines, cloud warehouses, and the data layer that models depend on.