ML Workbench unable to access Studio Knowledge Model resources

1. Does ML Workbench require separate permissions or resource assignments to access Studio Spaces or Knowledge Models?

Yes, ML Workbench requires its own application key permissions, separate from the individual user’s access, to view and use platform resources.

For Data Integration, the ML Workbench application key must be explicitly granted access to the relevant Data Pool and Data Model.

 

2. Can ML Workbench access Knowledge Models directly, or does it only work through Data Pools/Data Models?

Yes. ML Workbench can access Knowledge Models directly if its application key has the required Knowledge Model/Studio scopes and permissions. It is not limited to Data Pools/Data Models.
 

3. Has anyone encountered a situation where authentication succeeds but get_data_pools() and get_spaces() both return empty results?

Yes  successful authentication with empty listings usually indicates missing  permissions, not an authentication issue. The MLWB app key can connect successfully but still see no Data Pools or Studio Spaces if it hasn’t been granted access to those resources.
 

4. Is there any additional configuration required to make Studio resources visible inside ML Workbench?

There is no separate “make Studio visible in MLWB” setting. Access depends on the MLWB application key having the required scopes/permissions and access to the specific Studio Space, Data Pool/Data Model, or Knowledge Model being used.




I think the missing step is this: go to the Data Pool, click the three dots → Permissions, and assign the required permissions to your ML Workbench application. Once that is done, you should be able to see and access the relevant resources.

Hi, thank you for the detailed response. I appreciate it. So, I went into the studio knowledge model and granted full permission to ML Workbench and then i was able to retrieve the data model inside pycelonis. that solves my problem.