Manage AI-ready infrastructure
At a glance
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Learn to manage AI infrastructure by planning compute capacity, enforcing governance, configuring monitoring, and optimizing costs to support scalable, compliant, and high-performing AI workloads.
Prerequisites
- Familiarity with Azure infrastructure fundamentals including virtual machines, storage accounts, and resource groups
- Basic understanding of AI and machine learning workloads such as model training and inference
- Experience navigating the Azure portal or executing Azure CLI commands
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Modules in this learning path
Microsoft Foundry uses a hub-and-project architecture to centralize governance, security, and shared resources while enabling team autonomy. Hub-level connections, identities, and policies reduce cost, complexity, and duplication, supporting scalable, secure, enterprise-ready AI deployments.
This module covers Azure Monitor fundamentals, including metrics collection, dashboards, alert and processing rules, and KQL-based log queries. Together, these capabilities enable faster root-cause analysis and improve operational reliability.
This module shows how to secure AI agents using Azure RBAC and Microsoft Entra ID managed identities, eliminating stored credentials while enforcing least‑privilege access. It then demonstrates deploying Azure Cosmos DB for NoSQL as a scalable, compliant conversation store optimized for agent workloads.
This module explains building resilient, multi-region AI infrastructure using Microsoft Foundry hubs, geo-redundant storage, and Azure Container Registry geo-replication. It shows how coordinated replication and failover protect data, model images, and AI services during regional outages.