AI Model Serving: Production Deployment & Scaling

I will build production-grade serving infrastructure for your AI model or AI-powered application — covering containerised deployment with Docker, auto-scaling configuration for variable load, request queuing for compute-intensive operations, monitoring and logging, and a CI/CD pipeline for model or application updates. AI applications that work correctly in development frequently fail under production load when there is no infrastructure for handling concurrent requests, scaling under demand spikes, or recovering gracefully from individual request failures — production-grade serving infrastructure addresses each of these systematically rather than leaving them to be discovered as incidents.
The infrastructure covers Docker containerisation of your AI application, cloud deployment (AWS, GCP, or Azure) with auto-scaling configuration, request queue management for compute-intensive AI operations, health check and monitoring setup, structured logging for debugging and cost tracking, and a CI/CD pipeline for safe, automated deployment of updates.
Designed for AI applications and models that have proven their value in development and need professionally engineered infrastructure to handle real production traffic reliably and cost-effectively.