Platform and deployment questions

Direct answers on choosing between Azure, AWS, and GCP for an AI platform, and on the deployment practices that follow from that choice.

The right cloud depends on the AI workload, the data it depends on, and the systems it must connect to. We start by mapping model training, serving latency, and data residency needs against each provider's managed AI services and existing enterprise agreements.

Azure often fits organizations already invested in Microsoft identity and data estates. AWS offers the broadest set of infrastructure building blocks, while GCP is strong for data-heavy analytics and model tooling. Each choice is weighed against cost, team skills, and portability.

Deployment guidance starts with the serving architecture: container orchestration, model versioning, and CI/CD pipelines that test and promote models in the same way as application code. Infrastructure is sized from expected request volume and peak load, not from averages alone.

Data privacy controls, network isolation, and access policies are defined before the first pipeline runs. Planning decisions are then carried into a documented runbook, so the platform can be operated and changed after launch.