AI Studio#

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.

5-Step AI Studio Process of Candidate Set, Ground Truth, Models, Validation, and Publish

Candidate Set#

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

Ground Truth#

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.

Models#

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.

Validation#

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.

Publish#

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#

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: