Cloud Engineering

Bridging the gap between a notebook and a scalable, monitored AI service.

Overview

90% of AI models never reach production. Teams struggle with deployment, monitoring for drift, and managing the costs of high-scale LLM usage.

Capabilities

Implementing CI/CD for ML, setting up monitoring dashboards (Grafana/Prometheus), and optimizing infrastructure for cost and latency on AWS/Azure.

Outcomes

Reduced deployment cycles from weeks to hours, automated drift detection, and optimized cloud spending through efficient resource orchestration.