- Getting started
- Getting set up as a legacy user
- Getting set up as an Automation Cloud user
- Default user permissions
- Balance
- Clusters
- Concept drift
- Coverage
- Datasets
- General fields (previously entities)
- Labels (predictions, confidence levels, hierarchy, etc.)
- Models
- Streams
- Model Rating
- Projects
- Precision
- Recall
- Reviewed and unreviewed messages
- Sources
- Taxonomies
- Training
- True and false positive and negative predictions
- Validation
- Messages
- Administration
- Manage sources and datasets
- Understanding the data structure and permissions
- Create a data source in the GUI
- Uploading a CSV file into a source
- Create a new dataset
- Multilingual sources and datasets
- Enabling sentiment on a dataset
- Amend a dataset's settings
- Delete messages via the UI
- Delete a dataset
- Delete a source
- Export a dataset
- Using Exchange Integrations
- Preparing data for .CSV upload
- Model training and maintenance
- Understanding labels, general fields and metadata
- Label hierarchy and best practice
- Defining your taxonomy objectives
- Analytics vs. automation use cases
- Turning your objectives into labels
- Building your taxonomy structure
- Taxonomy design best practice
- Importing your taxonomy
- Overview of the model training process
- Generative Annotation (NEW)
- Dastaset status
- Model training and annotating best practice
- Training with label sentiment analysis enabled
- Train
- Introduction to Refine
- Precision and recall explained
- Precision and recall
- How does Validation work?
- Understanding and improving model performance
- Why might a label have low average precision?
- Training using Check label and Missed label
- Training using Teach label (Refine)
- Training using Search (Refine)
- Understanding and increasing coverage
- Improving Balance and using Rebalance
- When to stop training your model
- Using general fields
- Generative extraction
- Using analytics and monitoring
- Automations and Communications Mining
- Licensing information
- FAQs and more
Default user permissions
When a new user is created or added to a new project, they are granted certain default permissions. These permissions will be the same whether you are an existing user added to a project, or a new user that has been created by an existing user.
The default permissions for every user in a given project are 'View labels' and 'View sources'.
These essentially grant you the ability to access (non-sensitive) datasets within that project and see the messages (which belong to sources) as well as the labels associated with those messages.
These permissions will not grant you the ability to apply or remove labels within a dataset. To be able to do this, among many other things, you will need to be granted additional permissions by another user within the project that has the 'Modify users' permission.
If you are accessing Communications Mining™ via Automation Cloud, then admins on your cloud tenant will automatically have Admin access on Communications Mining™. This will grant them admin privileges on 'Sources permissions', 'Datasets permissions', 'Streams permissions', 'Users permissions', 'Buckets permissions', 'Integrations permissions', and 'Utility permissions'.
For a more detailed explanation of the different user permissions, see here.
To understand more about how to update a user's permissions, see here.
