- Introduction
- Setting up your account
- Balance
- Clusters
- Concept drift
- Coverage
- Datasets
- General fields
- Labels (predictions, confidence levels, label hierarchy, and label sentiment)
- Models
- Streams
- Model Rating
- Projects
- Precision
- Recall
- Annotated and unannotated messages
- Extraction Fields
- Sources
- Taxonomies
- Training
- True and false positive and negative predictions
- Validation
- Messages
- Access control and administration
- Manage sources and datasets
- Understanding the data structure and permissions
- Creating or deleting a data source in the GUI
- Preparing data for .CSV upload
- Uploading a CSV file into a source
- Uploading a PST file
- Creating a dataset
- Multilingual sources and datasets
- Enabling sentiment on a dataset
- Amending dataset settings
- Deleting a message
- Deleting a dataset
- Exporting a dataset
- Using Exchange integrations
- Email transform tags
- Model training and maintenance
- Understanding labels, general fields, and metadata
- Label hierarchy and best practices
- Comparing analytics and automation use cases
- Turning your objectives into labels
- Overview of the model training process
- Generative Annotation
- Dastaset status
- Model training and annotating best practice
- Training with label sentiment analysis enabled
- Understanding data requirements
- Train
- Introduction to Refine
- Precision and recall explained
- Precision and Recall
- How validation works
- Understanding and improving model performance
- Reasons for label 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
- Publishing and tagging a model version
- Deleting a published model
- Adding new labels to existing taxonomies
- Maintaining a model in production
- Human-in-the-Loop validation
- Model rollback
- Using general fields
- Generative extraction
- Using analytics and monitoring
- Automations and Communications Mining™
- Developer
- Uploading data
- Downloading data
- Exchange Integration with Azure service user
- Exchange Integration with Azure Application Authentication
- Exchange Integration with Azure Application Authentication and Graph
- Migration Guide: Exchange Web Services (EWS) to Microsoft Graph API
- Fetching data for Tableau with Python
- Elasticsearch integration
- General field extraction
- Self-hosted Exchange integration
- UiPath® Automation Framework
- UiPath® official activities
- How machines learn to understand words: a guide to embeddings in NLP
- Prompt-based learning with Transformers
- Efficient Transformers II: knowledge distillation & fine-tuning
- Efficient Transformers I: attention mechanisms
- Deep hierarchical unsupervised intent modelling: getting value without training data
- Fixing annotating bias with Communications Mining™
- Active learning: better ML models in less time
- It's all in the numbers - assessing model performance with metrics
- Why model validation is important
- Comparing Communications Mining™ and Google AutoML for conversational data intelligence
- Licensing
- FAQs and more
Publish and tag model versions in Communications Mining to preserve specific training states for production use or rollback.
You must have assigned the Source - Read and Dataset - Read permissions as an Automation Cloud user, or the View sources and View labels permissions as a legacy user.
Every time you train the platform on your data, that is, annotating any messages, a new version of the model associated with your dataset is created. As these models are large and complex, previous versions are not automatically stored in our databases, purely as the storage requirements would be incredibly large.
The latest version of the model will always be readily available, but users are able to publish a specific model version that they would like to save. They can also choose to tag published models with a Live or Staging tag.
Reasons for publishing a model version
- Publishing a model gives you determinism over predictions, particularly for when you are using Streams. This means that you can be confident of precision and recall scores for this version of the model, and future training events will not alter them.
- In the Validation page, you can view the validation scores for previous published model versions. This allows you to compare the scores over time, and notice how your training has improved your model.
Publishing a model version
To publish a model version:
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Navigate to the models page using the navigation bar.
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Select the publish toggle to save the current model version.
Result: The model version is published and saved in your database.
Updating the tag for a model version
To update the tag for a model version, proceed as follows:
- Select the arrow for Tags on any published model.
- Select Live or Staging, depending on the status of the published model in any downstream deployments.
Result: The tag is updated for the selected published model version.