Advent of 2024, Day 19 – Microsoft Azure AI – Azure AI Foundry management center

This article is originally published at https://tomaztsql.wordpress.com

In this Microsoft Azure AI series:

1. Dec 01: Microsoft Azure AI – What is Foundry?
2. Dec 02: Microsoft Azure AI – Working with Azure AI Foundry
3. Dec 03: Microsoft Azure AI – Creating project in Azure AI Foundry
4. Dec 04: Microsoft Azure AI – Deployment in Azure AI Foundry
5. Dec 05: Microsoft Azure AI – Deployment parameters in Azure AI Foundry
6. Dec 06: Microsoft Azure AI – AI Services in Azure AI Foundry
7. Dec 07: Microsoft Azure AI – Speech service in AI Services
8. Dec 08: Microsoft Azure AI – Speech Studio in Azure with AI Services
9. Dec 09: Microsoft Azure AI – Speech SDK with Python
10. Dec 10: Microsoft Azure AI – Language and Translation in Azure AI Foundry
11. Dec 11: Microsoft Azure AI – Language and Translation Python SDK
12. Dec 12: Microsoft Azure AI – Vision and Document AI Service
13. Dec 13: Microsoft Azure AI – Vision and Document Python SDK
14. Dec 14: Microsoft Azure AI – Content safety AI service
15. Dec 15: Microsoft Azure AI – Content safety Python SDK
16. Dec 16: Microsoft Azure AI – Fine-tuning a model
17. Dec 17: Microsoft Azure AI – Azure OpenAI service
18. Dec 18: Microsoft Azure AI – Azure AI Hub and Azure AI Project

The management center is a part of the Azure AI Foundry portal. It gives you the overview on activities and governance. From management center you can manage Azure AI Foundry hubs, projects, resources, access, endpoints, models and other resources and general settings.

General overview of management center:

Quota gives you capability to monitor and track your usage by subscription and region and per different models, like Azure OpenAI Standard, Azure OpenAI Provisioned, Azure OpenAI Global-Standard, Azure OpenAI Global-Batch.

You can use management center to create and configure both hubs and projects within hubs and view the resources. Both Hub and Project are similar within management center. You can access Users, Models, connected resources and compute.

Models + Endpoints will give you overview and monitoring of model versions, state additional filters, fine-tuning, capacities and rate limits (tokens per minute).

In connected resources you can create new connections or list all resources connected in selected hub.

In Compute you can spin up or shut down compute (VM machines) for purposes of your projects in the hub.

Creating is common task (same as in Fabric or Azure Machine Learning service).

And you can list all the compute instances or serverless instances.

Tomorrow we will look into the Models and endpoints in Azure AI Foundry.

All of the code samples will be available on my Github.

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