Service
MLOps & Deploy
From notebook to production. Versioned, monitored, auto-scaling, and quality-gated.
Overview
The gap between a working prototype and a reliable production system is where most AI projects stall. We bridge it with model versioning and lineage tracking, containerized GPU serving, CI/CD pipelines that block deployment when quality metrics drop, and live monitoring that catches accuracy drift before it becomes a business problem. Your models run with the same operational rigor as your core software, with clear SLAs, rollback capabilities, and cost visibility.
Capabilities
Model Serving & Registry
Optimized LLM serving and custom model endpoints, with every version tracked alongside its metadata, metrics, and lineage. Roll back to any previous version in under a minute if something goes wrong.
CI/CD for AI
Each code push triggers evaluation benchmarks against your golden dataset. If accuracy drops or latency regresses, the deploy is blocked automatically. Canary releases route a small percentage of traffic to the new version first.
Monitoring & Observability
Dashboards tracking latency percentiles, throughput, error rates, cost per request, and model accuracy over time. Alerts cover both infrastructure health and output quality, catching degradation before users notice.
Scaling & Cost Control
Auto-scaling based on traffic patterns with hard cost guardrails. Multi-region deployment for latency-sensitive applications. Spot GPUs for batch workloads keep infrastructure costs predictable and controlled.
Deliverables
- Production infrastructure with IaC (Terraform/Pulumi)
- CI/CD pipeline with model quality gates and automated eval
- Monitoring stack with accuracy drift detection and SLA reporting
Tech Stack
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