Scaling AI use cases in Azure

With increasing number of AI services in Azure that also integrates with other Azure services, the question about how to implement this at scale and fit it into the Cloud Adoption Framework for Azure arise.

To answer and solve this question, let's build automation on how to implement Enterprise Scale AI landing zones in Azure. These landing zones will accelerate any kind of AI use case in Azure. Machine Learning, Computer Vision, Generative AI or any of the other capabilities that Azure offer.
For example, if you plan to use Azure OpenAI you also need to deploy other supporting services such as Storage Accounts, Key Vault, Private Endpoints, Azure Application Gateway and Api Management to securely expose your deployments and services and to be able to monitor you models.

So, to make sure you harness the full potential of the Azure AI services, a robust and scalable infrastructure is crucial.
But, the infrastructure is just the first step. We also need to include MLOps, monitoring and other features depending on what the use case is.
Join me on this session and I'll show you my take on this!

Kim Berg

Cloud Solution Architect active in Data, AI & Cloud communitys.

Norrköping, Sweden

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