Summary
Unit 7 of 7 · Module 2: Select, deploy, and evaluate Microsoft Foundry models
Read this lesson
The ground we covered
That wraps up Module 2, where you took a model from the shelf all the way to a report card. You started in the catalog: more than ten thousand models from Microsoft, OpenAI, Meta, Mistral, Hugging Face, and more, with filters to cut the list down to a few. Benchmarks let you weigh them on quality, like accuracy and coherence; on safety, meaning how easily they can be pushed into harmful content; on cost against your budget; and on latency and throughput, for how fast they feel. Then you saw the ways to deploy: serverless, paying per call; provisioned, for steady heavy traffic; managed compute, on dedicated virtual machines; and batch, the cheap option for work that can wait. Each one trades off scaling, billing, and how much control you get. The playground let you try a model right away, without writing code. And evaluation ranged from manual testing, for things only a person can judge, like whether an answer feels helpful, to AI-assisted quality and safety metrics, and NLP scores like F1 and ROUGE that compare answers with ground truth. A full evaluation run then shows what a model does well, where it slips, and what to fix next.
Where the roads lead
From here, wire the model into your app, using the SDKs, the REST APIs, and the code samples in the portal. Ground its answers in your own documents with RAG, so our support chatbot quotes the real return policy instead of guessing. Put Azure AI Content Safety in front of it, to filter harmful requests and replies. Where it's supported, fine-tune a model for a specialized job. Once it's live, watch it with Azure Monitor and Application Insights: usage, latency, cost, and errors. And keep listening to real users, re-evaluating as you go. Choosing, deploying, and evaluating well is what every solid generative AI app is built on. Next, you'll build a chat app of your own with the Foundry SDK.
Facts from Microsoft's documentation: Microsoft Foundry Models overview; Model benchmarks and leaderboards in Microsoft Foundry; Understanding deployment types in Microsoft Foundry Models; Observability in generative AI (CC BY 4.0), rewritten in Loop's own words and examples. Narration, animation, and quiz by Loop. Microsoft, Azure, and Microsoft Foundry are trademarks of Microsoft. Loop is independent and not affiliated with or endorsed by Microsoft.