Deploying InterSystems IRIS on AWS or Azure is usually done using a main server with a backup server to ensure the system keeps running if one fails. For better performance, resources can be increased or more servers added when needed. Regular backups are taken and stored in cloud storage to prevent data loss. Monitoring tools are used to track system health and quickly detect issues. Overall, the setup focuses on reliability, scalability, and data safety.

Really interesting perspective, especially the “calculator” analogy. The point about developers needing to understand the work before relying too heavily on GenAI makes a lot of sense. I also agree with your thoughts on attribution and learning mode because long term skill development still matters.

I think the future will probably be a balance where developers use these tools more like assistants instead of replacements. There are also all-in-one APIs and tools now that provide access to multiple chat models in one place, which can make testing different workflows and technologies easier for developers.

Yes InterSystems IRIS Docker images are production ready and are commonly used in real production environments including Kubernetes deployments.

HealthShare products built on IRIS can also be used in production but they need proper setup especially for licensing data storage and integration components. It is important to follow InterSystems official deployment guidelines to ensure stability and support.