AI Services - MLOps
Production AI needs production infrastructure.
Getting models to run in notebooks is easy. Keeping them reliable in production is where most programs stall. We build the operating layer that keeps AI performing at scale.
The bottleneck is not the model. It is everything around it.
Model serving, CI/CD, drift detection, A/B testing, cost tracking, and integration architecture are what turn ML experiments into business systems.
The full MLOps stack.
Model Serving Infrastructure
Real-time, batch, and streaming inference with latency/cost optimization.
ML CI/CD
Automated training, validation, deployment, and rollback-ready model release flow.
Real-Time Monitoring
Accuracy, latency, errors, quality, and resource telemetry with alerting.
Drift Detection & Retraining
Statistical drift monitoring with automated retrain/validation/deploy pipelines.
A/B Testing Frameworks
Production model experiments with statistically grounded promotion decisions.
Enterprise Integration
API/connectors to ServiceNow, Salesforce, SAP, Microsoft 365, and custom systems.
Engagement Details
Typical engagement
Ongoing managed retainer
Delivery model
Monthly retainer with SLA definitions
Team composition
MLOps engineer + platform engineer + SRE
Scope
4-8 week infrastructure setup followed by managed operations