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.
Enterprise AI fails for reasons that have nothing to do with model quality. Without an internal platform, every team reinvents the same plumbing — subscriptions and networking, Azure OpenAI access and quotas, identity and policy, deployment pipelines, logging and cost controls — and the result is a sprawl of ungoverned prototypes that cannot be audited, scaled or operated safely. Platform engineering fixes this at the root: it treats AI enablement as a product with golden paths that teams follow by default, so security, governance and operational quality are inherited rather than bolted on.
This course is a hands-on, three-day build of that platform. It walks through the AI platform operating model — what a platform team is, how it enables rather than blocks — then constructs an Azure AI landing zone with the right subscription, networking and policy boundaries. From there you configure the model and API gateway (access, quotas, routing, secrets), wire identity and security with managed identity and RBAC, automate everything with DevOps and infrastructure-as-code, and close the loop with observability and FinOps dashboards that surface token usage, quality metrics and budget burn. The capstone ties it together: a reusable platform blueprint with a security baseline, deployment model and onboarding process your teams can adopt the following Monday.
You leave this course with more than notes: a working reference implementation of the platform, template and checklist assets you can reuse, and a clear picture of the team and operating model required to run it. It is built for platform engineers, DevOps leads and cloud architects who own the Azure estate and want GenAI adoption to follow a governed path — as well as the engineering managers who need to justify the platform investment and sequence the work. By the final day you will be able to explain, to both engineers and leadership, exactly how AI workloads will be onboarded, secured, observed and cost-controlled in your organisation, and why a platform is the cheapest way to deliver that.
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.