Question
Secret and SSH management in Machine Learning workbench
Hello Celonis and others,
I am trying out the Machine Learning workbench, and in the examples I see the API secrets out in the open. I would prefer to hide the secrets in a secret management system. Does the Machine Learning workbench support something like that, and how can I setup a secret management system?
Inspiration for in my opinion good secret management is in:
https://docs.databricks.com/security/secrets/index.html
For managing SSH keys inspiration can be found in:
https://help.github.com/en/github/authenticating-to-github/adding-a-new-ssh-key-to-your-github-account
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theskumar/python-dotenv
Get and set values in your .env file in local and production servers. :tada: - theskumar/python-dotenv
This way you will have the secrets present in your execution environment, but not inside your source code.
Note that you can also set permissions on who can access a specific Workbench as well.
Does that help? If there is anything else we can help you with I am happy to discuss your Use Cases over a quick call.
Best
Nicolas
(Product Management ML Infrastructure)