Plan and Prepare to Develop AI Solutions on Azure
This Learn Live session focused on the work that happens before the demo.
When developers build AI solutions, it is tempting to jump straight into prompts, models, and sample code. But the earliest decisions shape everything that follows: which Azure AI services to use, how to prepare the development environment, what Azure AI Foundry gives the team, and where responsible AI considerations enter the workflow.
I wanted the session to make planning feel like engineering, not paperwork. Service choice, SDK choice, safety thinking, and environment setup are architecture decisions because they determine what the team can build, debug, and operate later.
The session also changed how I think about teaching. A good technical explanation should not only show the next command. It should help the reader understand why one path is a better fit than another.
That is the lesson I keep returning to: planning is useful when it improves the quality of future implementation decisions.
Watch the session:
Learn Live: Plan and Prepare to Develop AI Solutions on Azure
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