Google Cloud Platform (GCP)

Google Cloud resources are organized through projects and related organizational controls. Service accounts and IAM govern access; regions and networks govern placement and connectivity. A workload may combine object storage, analytical databases, container services and managed ML capabilities. These components have different lifecycle, quota and execution semantics. Google Cloud is broader than Vertex AI or Cloud Run, which address particular workflows. Platform engineering coordinates those services with data ownership, observability and recovery. Managed operation shifts selected responsibilities to the provider while leaving application correctness and access decisions with the team.

The practitioner establishes resource organization and service identities, chooses locations and selects compute and storage from the workload's data and latency needs. They define permissions and network paths, automate configuration and measure operational behavior. They budget for retained resources and data movement alongside execution. Useful work produces a documented deployment with checked access and recovery, including an explanation of why particular managed services were selected and what would trigger a capacity or architectural change.

Projects are organizational boundaries, but their existence alone does not ensure narrow access or isolation. Service accounts can be overprivileged, and cross-region data movement can add latency or cost. Service-specific availability and quotas must be checked. Inspect identities, resource ownership, failure behavior and actual billing dimensions. Google Cloud proficiency concerns its operational mechanisms and integrations, distinct from the generic knowledge of cloud architecture or one managed ML API.