Soft-deleted workspace blocking recreation
usabilityActiveStableThe user cannot recreate an Azure ML workspace due to a soft-deleted state that cannot be purged or recovered.
Score Breakdown
Heuristic ranking from public discussion signals — not a validated prediction of commercial opportunity, demand, or willingness to pay.
Composite 65/100 (High, unvalidated). Top driver: Severity (25% weight, 20 pts).
Heuristic only — often urgency map or random scaffolding on ingest, not measured mention frequency. Maps to XPS relevance (with market size).
LLM/mock judgment of intensity from title/summary text — not ops or ticket data. Maps to XPS quality (with willingness to pay).
LLM/mock purchase-intent guess from text — not invoices, surveys, or paid seats. Maps to XPS quality.
Heuristic/scaffold (often random or fixed on insert) — not a verified mention trajectory. Maps to XPS novelty.
Heuristic/scaffold (often random or fixed) — not TAM research. Maps to XPS relevance (with frequency).
Catalog notes (not predictive analysis)
Soft-deleted workspace blocking recreation (usability). Catalog heuristic opportunity score: 65/100 — a chosen formula over discussion-signal facets, not evidence of demand, conversion, or willingness to pay. Treat as browsing rank, not a commercial prediction.
The user cannot recreate an Azure ML workspace due to a soft-deleted state that cannot be purged or recovered.
Source Examples
“Azure ML workspace stuck in soft-deleted state: create blocked, permanently-delete cannot find workspace ### Describe the bug Azure CLI Version: 2.88.0 ML Extension: 2.44.1 Subscription: <subscription-id> Resource Group: stmlwus2ws01-rg Workspace: stmlwus2ws01 Problem: I deleted an Azure ML workspace. When I attempt to recreate the workspace, deployment fails with: "Soft-deleted workspace exists. Please purge or recover it." However, attempting to permanently delete the workspace fails because the workspace cannot be found: az ml workspace delete \ --name myws \ --resource-group my-rg \ --subscription <subscription-id> \ --permanently-delete \ --yes Error: (UserError) Unable to find workspace: /subscriptions/.../resourcegroups/my-rg/providers/Microsoft.MachineLearningServices/workspaces/myws This creates a deadlock: - Workspace creation fails because a soft-deleted workspace exists. - Permanent delete fails because the workspace cannot be found. - No CLI command appears available to enumerate or purge the soft-deleted workspace. Expected behavior: Either: 1. `az ml workspace delete --permanently-delete` should purge the soft-deleted workspace, or 2. The CLI should provide a supported command to list and purge soft-deleted workspaces. Actual behavior: Workspace name remains blocked but cannot be purged. ### Related command az ml workspace delete \ --name myws \ --resource-group my-rg \ --subscription <subscription-id> \ --permanently-delete \ --yes ### Errors (UserError) Unable to find workspace: Code: UserError Message: Unable to find workspace: ### Issue script & Debug output > cli: Received HttpResponseError: Traceback (most recent call last): File "/home/myubuntuuser/.azure/cliextensions/ml/azext_mlv2/manual/custom/workspace.py", line 278, in ml_workspace_delete del_result = ml_client.workspaces.begin_delete( name=name, delete_dependent_resources=all_resources, permanently_delete=permanently_delete ) File "/home/myubuntuuser/.azure/cliextensions/ml/azure/ai/ml/_telemetry/activity.py", line 288, in wrapper return f(*args, **kwargs) File "/opt/az/lib/python3.14/site-packages/azure/core/tracing/decorator.py", line 119, in wrapper_use_tracer return func(*args, **kwargs) File "/home/myubuntuuser/.azure/cliextensions/ml/azure/ai/ml/operations/_workspace_operations.py", line 355, in begin_delete return super().begin_delete( ~~~~~~~~~~~~~~~~~~~~^ name, delete_dependent_resources=delete_dependent_resources, permanently_delete=permanently_delete, **kwargs ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ) ^ File "/home/myubuntuuser/.azure/cliextensions/ml/azure/ai/ml/operations/_workspace_operations_base.py", line 494, in begin_delete workspace: Any = self.get(name, **kwargs) ~~~~~~~~^^^^^^^^^^^^^^^^ File "/home/myubuntuuser/.azure/cliextensions/ml/azure/ai/ml/_telemetry/activity.py", line 288, in wrapper return f(*args, **kwargs) File "/opt/az/lib/python3.14/site-packages/azure/core/tracing/decorator.py", line 119, in wrapper_use_tracer return func(*args, **kwargs) File "/home/myubuntuuser/.azure/cliextensions/ml/azure/ai/ml/operations/_workspace_operations.py", line 143, in get return super().get(workspace_name=name, **kwargs) ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/myubuntuuser/.azure/cliextensions/ml/azure/ai/ml/operations/_workspace_operations_base.py", line 91, in get obj = self._operation.get(resource_group, workspace_name) File "/opt/az/lib/python3.14/site-packages/azure/core/tracing/decorator.py", line 119, in wrapper_use_tracer return func(*args, **kwargs) File "/home/myubuntuuser/.azure/cliextensions/ml/azure/ai/ml/_restclient/v2024_10_01_preview_tsp/operations/_operations.py", line 10422, in get map_error(status_code=response.status_code, response=response, er”
Competitive Landscape
- Existing solutions are either too expensive or too limited
- Most competitors target enterprise, leaving mid-market underserved
- Community scripts and manual processes are the primary alternative
Recommended Next Steps
- ✓Validate pain intensity with 5-10 target customer interviews
- ✓Build minimal viable solution addressing the core workflow
- ✓Test pricing with early adopters from community forums
Related Pain Points
Target Customers
- IT teams at mid-size organizations (100-2000 employees)
- MSPs and consultants managing multiple client environments
- Teams without dedicated specialist staff for this domain
Monetization Ideas
- 1SaaS subscription model ($99-$499/month depending on scale)
- 2Usage-based pricing aligned with value delivered
- 3Freemium tier to drive adoption and prove value