Deployed is not done
Models drift as real-world data shifts. Infrastructure costs creep up. The engineer who understood the system leaves. Without ongoing maintenance, even strong AI implementations degrade from assets into liabilities.
I provide hands-on AI operations support — monitoring, retraining, cost control, and knowledge transfer — so your systems keep delivering value long after launch.
How it works
- Monitoring setup: Baselines for accuracy, latency, throughput, and cost. Alerts fire when metrics drift beyond acceptable thresholds.
- Drift detection: Track concept drift, data drift, and prediction drift. When detected, I investigate root causes and fix through retraining, feature engineering, or architecture changes.
- Scheduled maintenance: Retrain on clear performance triggers. A/B test new versions against production. Full rollback capability and version control of training data.
- Infrastructure tuning: Vector database query tuning, embedding strategy improvements, cloud resource right-sizing. Keep costs proportional to value.
- Knowledge transfer: Runbooks, team training, gradual handoff of routine maintenance to your internal team. The goal is independence, not a retainer dependency.
What you get
- Real-time monitoring dashboards tracking the metrics that matter
- Drift detection with documented remediation protocols
- Retraining pipeline with A/B testing and rollback
- Infrastructure cost audit with specific savings targets
- Operational runbooks your team can follow without me
Who this is for
You have AI in production but no dedicated MLOps expertise internally. Especially useful in the first 6-12 months after deployment — when drift, scaling issues, and knowledge gaps hit hardest. Also valuable when key team members leave and you need to preserve continuity.
AI maintenance is a natural follow-on to an LLM Residency or AI Integration engagement — once systems ship, they need ongoing care.
Book a free call
Schedule a maintenance consultation to discuss your AI operations challenges.