This section provides reference and how-to information for Squirro’s AI Studio, a no-code machine learning platform that enables you to build and deploy machine learning models without writing code.
The Candidate Set helps you to find good text extracts for your ground truth in a large data universe.
For more information, see AI Studio Step 1: Candidate Sets
The ground truth is the set of documents that you want to use to train your model. You can use the Ground Truth to train a model to predict the category of a document.
To learn more, see AI Studio Step 2: Ground Truth.
The Models section of AI Studio enables you to train machine learning models by selecting templates without the need for writing code.
For more information, see AI Studio Step 3: Models.
The Validation section of AI Studio enables you to validate the performance of your machine learning models.
See AI Studio Step 4: Validation for more information.
The Publish section of AI Studio enables you to publish your machine learning models to the Squirro platform.
To learn more, see AI Studio Step 5: Publish.
Bulk labeling allows you to accelerate the process of creating an initial Ground Truth based on previously-defined proximity rules.
To learn more, see Bulk Labeling.
ML Enrichments for Pipeline Workflows#
The ML Enrichments for Pipeline Workflows enable you to deploy your AI Studio-built machine learning models to enrich your data.
For more information, see ML Enrichments for Pipeline Workflows.
How To Guides#
AI Studio guides include the following: