- 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)
- Understanding the status of your dataset
- 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
Using Exchange Integrations
User permissions required: ‘Sources Admin', 'Modify Integrations', 'Update Data to Buckets' , AND 'Datasets Admin'.
It's important to understand the relationship between key platform components such as integrations, mailboxes, buckets, sources, and datasets to set up your data effectively.
First, an Exchange 'Integration' is set up (via a Service Account), with data being synced from the Microsoft Exchange Server. This integration allows you to sync multiple 'mailboxes'.
These mailboxes are each stored in a 'bucket', and each bucket can contain multiple mailboxes.
Next, you will need to set up a 'source'. This is a collection of raw annotated communications data of a similar type. When setting up a source, if you are using data from an email integration you must specify which bucket you want to sync from (i.e. - the bucket where the mailboxes in scope for your use case are stored).
Once you have finished setting up your source, you will need to add your source to a 'dataset', which is where your Model will be trained.
Each dataset belongs to a 'project', which is a permissioned storage area within the platform. Each dataset and source belongs to a specific project, which is designated when they are created.
The following diagram illustrates how all these components are related:
At a high level, you will need to complete the following steps (in this specific order) to be able to have the data from your mailboxes show up in the platform: