From Prototype to Production: Architecting Scalable AI Systems (Part 2)

You built a working AI prototype—great. Now comes the moment of truth: real users. Most failures don’t come from the model; they come from the system around it. A prototype is a single-player demo; a product is a multi-user system. This piece shows how to make that shift: think in four clean layers—Interface, API Logic, AI Services, and Data/Infrastructure—so each part can scale, fail, and recover independently. Choose your footing wisely (cloud for speed, on-prem for control, hybrid when clients or workloads demand both). Containerize everything, split services, and orchestrate with Kubernetes in production and Docker Compose in dev. Optimize for reliability and speed with quantization, caching, batching, circuit breakers, and autoscaling on queue depth—not just CPU.

Data is where products quietly break. Start lean: collect only what drives value, validate at the edges, version your datasets, and keep pipelines simple (S3/GCS + Postgres/Firestore + small scheduled jobs). For modeling, follow the good-enough rule: ship when business KPIs are met. Fix data before hyperparameters; use transfer learning and track every experiment for reproducibility.

Lightweight MLOps keeps you safe: version code/data/models, automate CI/CD with GitHub Actions + Docker, deploy blue-green, monitor accuracy, latency, and business metrics, detect drift, and roll back fast. With a focused 30-day push—load tests, data hardening, monitoring, and rollback—you turn a fragile MVP into a dependable, scalable product.

Read the full piece https://iexchange.substack.com/p/from-prototype-to-production-architecting