Premium Azure AI platform course
AI Platform Engineering on Microsoft Azure
Build the internal platform for safe, repeatable enterprise AI delivery: landing zones, model access, identity, policy, DevOps, observability, FinOps and developer experience.
Platform Engineering Outcomes
Participants learn how to standardize model access, networking, identity, policy, logging, release gates and cost controls so multiple teams can build AI applications without reinventing the platform.
Design AI landing zones
Plan subscriptions, resource groups, network access, private endpoints, policy and shared services.
Govern model access
Define gateways, quotas, routing, identity, RBAC, secrets, audit and content filter patterns.
Enable delivery teams
Create templates, service catalogs, dashboards, documentation and support practices for reuse.
Course Modules
AI platform operating model
Platform team role, product mindset, shared services and enablement models.
AI landing zone
Subscriptions, resource groups, networking, private access and Azure policy.
Model and API gateway
Azure OpenAI access, quotas, model routing, secrets and gateway controls.
Identity and security
Managed identity, RBAC, data access, audit and content safety baselines.
DevOps and IaC
Templates, environments, CI/CD, approvals and release gates.
Observability and FinOps
Logs, traces, token usage, quality metrics, budgets and platform dashboards.
Capstone
Design an Azure AI platform blueprint with landing zone, security baseline, deployment model, observability and team onboarding process.
Included templates
- Azure AI platform reference architecture
- Landing zone checklist
- AI service catalog template
- Platform governance checklist
Build A Reusable Azure AI Platform
Tell us your Azure setup, security model and target teams. The workshop can be tuned for architecture offices, platform teams or AI enablement groups.