Building an AI feature can mean juggling data, models, deployments, and monitoring. Microsoft Azure Foundry brings those jobs into one studio. An AI Foundry resource provides access to models and related capabilities; projects organize the assets for a particular use case, such as a book recommendation engine. section 7.4 section 7.4.1
A practical route from idea to endpoint is:
- Create an AI Foundry resource, choosing a subscription, resource group, and available region. Create a clearly named project for the use case; its workspace brings data connections, jobs, and endpoints into view. section 7.4.1
- Connect the project to its data. For reviews in Blob Storage, add the storage account under Management Center → Connected Resources, choose Microsoft Entra ID authentication, and grant the workspace’s managed identity the needed Storage Blob Data Contributor role. section 7.4.2
- Prepare labeled input–output pairs as JSONL, choose a base model in the Fine-Tuning wizard, and submit a job. Review its results before deploying the model as an inference endpoint. section 7.4.4
Recommendations from customer reviews
A review praising intricate world-building and moral dilemmas can be paired with an ideal recommendation for another speculative novel. Such pairs give the fine-tuning job concrete examples of the response the application should produce. section 7.4.4
An endpoint is not the finish line. Deployment pipelines can add staging tests and human approval before production; monitoring tracks latency, errors, drift, and fairness afterward. Those pipeline options are outlined rather than configured step by step. section 7.4.5
