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- Getting started
- 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
- Selecting label confidence thresholds
- Create a stream
- Update or delete a stream
- Licensing information
- FAQs and more
Communications Mining User Guide
Last updated Nov 19, 2024
Update or delete a stream
User permissions required: ‘Modify streams’ (to update) OR ‘Streams admin’ (to delete).
UPDATE: 'Streams' were previously known as 'Triggers' in the platform. They've been renamed to better reflect the functionality that they provide for our users. This name change won't affect existing API routes. The trigger routes still exist, and new streams routes have been added that are identical in functionality. We'll gradually migrate our customers to these new streams routes in the coming months.
There are a few reasons why you should update a stream:
- You want to adjust the thresholds for the same model version, to change the output of predictions.
- When a new model version is created via additional training and you want to leverage the output from the latest (pinned) version, then you should update the stream to include the new version.
Note: If updating a stream to a new pinned model version, you should also adjust all of the label thresholds to maximise performance
from the new version.
- Navigate to the Streams page.
- Click the cog button in the right-hand corner of the stream card that you want to update.
- Amend any of the fields in the ‘Edit stream’ modal.
- Most commonly, it will be adjusting thresholds, or changing the pinned model version that the stream is based on.
- This includes updating thresholds, which should be done whenever switching to a new pinned model version.
- Click the tick button to save your changes:
- As soon as you do, the updates will take effect in the stream
Edit stream modal
- Follow the process above but instead of changing any of the stream’s properties, click the delete (bin) button.
- Then hit confirm in the ‘Delete stream’ modal to confirm your decision.
Note: To delete a stream, you will require the ‘Streams admin’ permission, whereas to update a stream, you only require the ‘Modify
streams’ permission.
Delete stream modal
