Showing tag results for containers

AI agents are quickly moving from experiments to production‑critical components of modern applications. But while many teams know how to build agents, far fewer are confident they’re hosting them on the right foundation. Most organizations start by deploying agents the same way they deploy microservices—containers, functions, or app services. ...

The path from prototype to production for AI/ML workloads is rarely straightforward. As data pipelines expand and model complexity grows, teams can find themselves spending more time orchestrating distributed compute than building the intelligence that powers their products. Scaling from a laptop experiment to a production-grade workload still feel...

Customers are looking to modernize their application on cloud and ask us for guidance on which Azure container services to choose. Should you choose A or B container services or A+B container services? Some choose both (A + B) or use A or B approach. Instead of trying to force fit applications on a container service(s), in this blog post, we sh...