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Model Mastery Workshops

Open in GitHub Codespaces

A growing series of 3-4 hr, self-guided technical workshops that teach you how to ship production-grade AI applications on Microsoft Foundry — one model family at a time. Workshops target L200–L400 learners (comfortable with Python, terminals, and cloud basics) and are designed for both live delivery and asynchronous self-paced study.

Each workshop is opinionated about the same end-to-end arc so the muscle memory transfers from one model family to the next:

Phase Description
Setup Provision a Foundry project, deploy the models the workshop needs, and verify your environment from GitHub Codespaces.
Build Implement a realistic scenario (often an agent) using the model family's flagship capabilities through Foundry's SDKs.
Explore Go deeper on the model family's specialised capabilities (embeddings, rerank, tool use, long context, vision, etc.) and connect them back to the scenario.

Workshops in the series

# Workshop Model family Status Start here
1 Anthropic on Foundry Claude family ☑️ WIP anthropic/
2 Cohere on Foundry Command A, Embed v4, Rerank v4 ✅ Ready cohere/README.md
3 Intro To Foundry Cross-model platform features ☑️ WIP foundry/

Each workshop is fully self-contained. You can start with whichever model family you care about — there are no cross-workshop prerequisites.


What's in a workshop folder

Every workshop follows the same top-level shape so you always know where to look:

<model-family>/
├── README.md            # workshop overview — start here
├── sample.env           # canonical environment-variable reference
├── requirements.txt     # Python dependencies for the workshop
├── lab-0-setup/         # provisioning + env wiring (Codespaces-first)
├── lab-1-<scenario>/    # build a realistic application with the flagship model
└── lab-2-<deep-dive>/   # explore the model family's specialised capabilities

Open the workshop's README.md first — it links to lab-0-setup/SETUP.md, which walks you through provisioning in roughly 15–20 minutes.

📝 Environment variables. Every workshop ships a sample.env in its top-level folder that documents every variable the labs read (Foundry endpoint, deployment names, API keys, optional load-test knobs, etc.). Lab 0's setup-env.sh copies it to .env and fills in concrete values for you. Read sample.env first whenever you want to know what a workshop expects to find in its environment.


Prerequisites (common to all workshops)

  • An Azure subscription with permission to create a Foundry account, project, and model deployments.
  • GitHub Codespaces (recommended) or a local environment with Python 3.11+ and the Azure CLI.
  • Familiarity with Jupyter notebooks and the Python ecosystem.

Each workshop's lab-0-setup/SETUP.md lists any model-family-specific extras (regions, quotas, SDK versions).


Contributing

The workshops live side-by-side so improvements to one (notebook style, evaluator patterns, tracing setup, Codespaces hardening) can flow easily to the others. If you spot a gap or an inconsistency between workshops, open an issue or PR against the affected <model-family>/ folder.