e.g. This is primarily aimed at first- and second-year undergraduates interested in engineering or science, along with high school students and professionals with an interest in programming.Securing and operating hybrid AI infrastructure demands identity-based access controls, end-to-end observability, resilient data pipelines, and intelligent automation. This course builds the skills to own security, integration, and operational decisions across production AI platforms.

You'll configure Workload Identity federation and Azure Key Vault to eliminate embedded credentials, build Grafana dashboards for GPU and latency metrics, and automate compliance remediation using Azure Resource Graph. You'll construct Data Factory pipelines with schema drift handling, integrate edge alerts with ServiceNow via Logic Apps, and implement event-driven autoscaling using KEDA and Azure Functions. By the end of this course, you'll define security boundaries for container workloads, set observability and compliance standards, design resilient data pipelines, own integration SLAs, and justify automation strategies with documented ROI. Designed for platform engineers securing, integrating, and automating hybrid AI infrastructure. A solid understanding of cloud security fundamentals and experience with automation tools are expected.