az ml job cancel --name <job-name> --resource-group <resource-group> --workspace-name <workspace>Delete or remove ml job with the Azure CLI; verify scope and backup/rollback before running.
Search an az command, command group, or Azure task. AzureGlossary shows what the command touches, how risky it is, which concepts explain it, and what safer checks and generated safe-first paths to run first.
Microsoft Learn remains the official Azure CLI reference. This page is the plain-English command map for safety, context, operator review, and safe-first command paths.
az ml job commands for AI and Machine Learning; browse safety labels, operation lanes, mapped Azure concepts, and safe-first examples.
az ml job cancel --name <job-name> --resource-group <resource-group> --workspace-name <workspace>Delete or remove ml job with the Azure CLI; verify scope and backup/rollback before running.
az ml job cancel --name <job-name> --workspace-name <workspace-name> --resource-group <resource-group>Delete or remove ml job with the Azure CLI; verify scope and backup/rollback before running.
az ml job create --file <job-yaml> --resource-group <resource-group> --workspace-name <workspace>Create, update, or operate ml job with the Azure CLI.
az ml job create --file <job.yml> --resource-group <resource-group> --workspace-name <workspace>Create, scale, or change cost-impacting configuration for ml job.
az ml job create --file evaluation.yml --workspace-name <workspace> --resource-group <group>Create, update, or operate ml job with the Azure CLI.
az ml job create --file job.yml --workspace-name <workspace-name> --resource-group <resource-group>Create, scale, or change cost-impacting configuration for ml job.
az ml job create --file job.yml --workspace-name <workspace> --resource-group <group>Create, update, or operate ml job with the Azure CLI.
az ml job create --file pipeline.yml --resource-group <resource-group> --workspace-name <workspace>Create, scale, or change cost-impacting configuration for ml job.
az ml job create --file pipeline.yml --workspace-name <workspace-name> --resource-group <resource-group>Create, scale, or change cost-impacting configuration for ml job.
az ml job create --file pipeline.yml --workspace-name <workspace> --resource-group <group>Create, update, or operate ml job with the Azure CLI.
az ml job create --file sweep-job.yml --resource-group <resource-group> --workspace-name <workspace>Create, update, or operate ml job with the Azure CLI.
az ml job download --name <evaluation-job> --workspace-name <workspace> --resource-group <group> --download-path ./outputsInspect or list ml job with the Azure CLI before making changes.
Start with a command, group, or messy task. The Command Center gives you a briefing before you copy anything: risk, touched services, safe-first paths, related concepts, and the official Microsoft Learn reference.
Try a real command such as az role assignment list, a group such as az aks, or a task such as private endpoint. Results open as command briefing cards.
Each card explains the command, what it touches, whether it is safe for discovery, and the safe-first path to run before risky changes.
Try a command group such as az group, az storage, az webapp, az aks, az sql, or az role assignment.
Inspect AKS state, credentials, node pools, upgrades, monitoring, and destructive operations from one lane.
Navigate database, analytics, messaging, and AI commands from discovery to controlled mutation.
Inspect identities and assignments before making privileged RBAC, managed identity, or credential changes.
Start with read-only discovery, create only after context checks, then clean up deliberately.
Discover storage accounts, then check network, identity, lifecycle, container, and destructive cleanup commands.
Move from App Service discovery through deployment, slot checks, diagnostics, and guarded rollback.