# 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. [](https://s3.amazonaws.com/download.squirro.net/docs/technical/ai-studio/ai-3.png)Looking to get started right away? To get started building your own no-code machine learning classification models, check out Squirro’s [How to Create a Document-Level Classifier](how-to/how-document-level.md#ai-studio-how-document) and [How to Create a Sentence-Level Classifier](how-to/how-sentence-level.md#ai-studio-how-sentence) guides. ## Step 1: 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. This includes using candidate sets to help you to find good text extracts for your ground truth in a large data universe. To learn more, see [Ground Truths](ground-truth.md#ai-ground-truths). ## Step 2: Models Overview The _Models Overview_ section of AI Studio enables you to train machine learning models by selecting templates without the need for writing code. Once selected and configured, you can then validate your models by testing their performance. For more information, see [Models Overview](models.md#ai-studio-models). ## Step 3: Published Models The _Published Models_ section of AI Studio shows your published machine learning models and allows you to deploy them within the Squirro platform directly from AI Studio. To learn more, see [Published Models](publish.md#ai-studio-publish). ## Share Labeling To grant access to your ground truth labeling to non-administrative users, you can share your labeling with them. To learn more, see [Share Labeling Permissions](share-labeling.md#ai-studio-share-labeling). ## 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](bulk-labeling.md#ai-studio-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](enrichments-workflows.md#ai-studio-enrichments-workflow). ## Squirro Client Reference For information on AI Studio use with Squirro Client, see [Squirro Client Reference](squirroclient-reference.md#ai-studio-sc-reference). ## How To Guides AI Studio guides include the following: - [How to Use Bulk Labeling in AI Studio](how-to/bulk.md#ai-studio-bulk-how) - [How to Create a Document-Level Classification Model in AI Studio](how-to/how-document-level.md#ai-studio-how-document) - [How to Create a Sentence-Level Classification Model in AI Studio](how-to/how-sentence-level.md#ai-studio-how-sentence) - [How to Integrate a Custom ML Classifier](how-to/integrate-ml-class.md#ai-studio-integrate-ml-class) - [How to Interact with Squirro Using Jupyter Notebook](how-to/jupyter.md#ai-studio-how-jupyter)