AI isn’t magic. Behind every model in production lies a thoughtfully crafted architecture that enables scale, speed, and stability. Over the last few years, I’ve been fortunate to architect AI-powered platforms using Databricks, Azure ML, Kafka, and containerised pipelines that can handle millions of data points in real-time.

A few lessons from the trenches:

• Data is the real bottleneck – Not the models. Build robust ingestion, validation, and lineage early on.

• MLOps isn’t optional – Treat your models like code. Automate training, versioning, and deployment.

• Think cloud-native – Containerise your workloads with Docker/Kubernetes and scale using autoscaling clusters (Databricks is brilliant for this).

• Focus on observability – Monitoring drift, latency, and prediction accuracy is crucial for production AI.

In one project for a major utility, we used Databricks notebooks + Delta Lake + Azure ML endpoints to deliver predictive maintenance at scale—reducing unplanned outages significantly. It wasn’t flashy AI—but it delivered business value, fast.