Production AI Platform

Challenge

Experimentation and production AI were sharing a path. A model that worked in a notebook had no safe way into live traffic: serving, rollback, and observation were assembled per workload, so a bad release could affect the consumer product.

Decision

Put canary rollout at the gateway rather than beside each model. Routing, rollback, and observation then lived in one control plane — a bad model could be pulled back without rebuilding serving for every team.

Approach

The platform work centered on how models are served, how inference is routed, how releases move into production, and how the system is observed. The scope stayed infrastructure — not model training.

Technical Delivery

Implemented production infrastructure for machine-learning and generative-AI workloads on Kubernetes, including model-serving, deployment workflows, inference routing, and observability. After the work, model changes moved through a gated serving path instead of one-off deploys.

Outcome

Models moved to production through a standard path — serving, routed traffic, controlled rollout, and observability — instead of bespoke infrastructure per model.

Technologies

• Kubernetes
• Model serving
• LLM inference
• AI gateways
• Canary deployments
• Observability

Related services

• AI Platform & MLOps
• Kubernetes
• Production readiness